{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- 将数据集存入一个名为chipo的数据框内\n",
    "# -- 查看前10行内容\n",
    "# -- 数据集中有多少个列(columns)？\n",
    "# -- 打印出全部的列名称\n",
    "# -- 数据集的索引是怎样的？\n",
    "# -- 被下单数最多商品(item)是什么?\n",
    "# -- 在item_name这一列中，一共有多少种商品被下单？\n",
    "# -- 在choice_description中，下单次数最多的商品是什么？\n",
    "# -- 一共有多少商品被下单？\n",
    "# -- 将item_price转换为浮点数\n",
    "# -- 在该数据集对应的时期内，收入(revenue)是多少？\n",
    "# -- 在该数据集对应的时期内，一共有多少订单？\n",
    "# -- 每一单(order)对应的平均总价是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#将数据集存入一个名为chipo的数据框内\n",
    "chipo = pd.read_csv('data/chipotle.tsv',sep='\\t')\n",
    "chipo.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4622, 5)\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "#数据集中有多少个列(columns)？\n",
    "print(chipo.shape)\n",
    "print(chipo.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
      "       'item_price'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(chipo.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=4622, step=1)\n"
     ]
    }
   ],
   "source": [
    "print(chipo.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chicken Bowl</th>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Burrito</th>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Guacamole</th>\n",
       "      <td>506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Burrito</th>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canned Soft Drink</th>\n",
       "      <td>351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips</th>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Bowl</th>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottled Water</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Fresh Tomato Salsa</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canned Soda</th>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Salad Bowl</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Soft Tacos</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Side of Chips</th>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Burrito</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbacoa Burrito</th>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Bowl</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Bowl</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbacoa Bowl</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Burrito</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Soft Tacos</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6 Pack Soft Drink</th>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Tomatillo Red Chili Salsa</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Crispy Tacos</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Tomatillo Green Chili Salsa</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Soft Tacos</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Crispy Tacos</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Tomatillo-Green Chili Salsa</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Salad Bowl</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nantucket Nectar</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Tomatillo-Red Chili Salsa</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbacoa Soft Tacos</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Roasted Chili Corn Salsa</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Izze</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Salad Bowl</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Roasted Chili-Corn Salsa</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbacoa Crispy Tacos</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbacoa Salad Bowl</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicken Salad</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Crispy Tacos</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Soft Tacos</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burrito</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Salad</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Salad Bowl</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowl</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Steak Salad</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salad</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crispy Tacos</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chips and Mild Fresh Tomato Salsa</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnitas Salad</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Veggie Crispy Tacos</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       quantity\n",
       "item_name                                      \n",
       "Chicken Bowl                                761\n",
       "Chicken Burrito                             591\n",
       "Chips and Guacamole                         506\n",
       "Steak Burrito                               386\n",
       "Canned Soft Drink                           351\n",
       "Chips                                       230\n",
       "Steak Bowl                                  221\n",
       "Bottled Water                               211\n",
       "Chips and Fresh Tomato Salsa                130\n",
       "Canned Soda                                 126\n",
       "Chicken Salad Bowl                          123\n",
       "Chicken Soft Tacos                          120\n",
       "Side of Chips                               110\n",
       "Veggie Burrito                               97\n",
       "Barbacoa Burrito                             91\n",
       "Veggie Bowl                                  87\n",
       "Carnitas Bowl                                71\n",
       "Barbacoa Bowl                                66\n",
       "Carnitas Burrito                             60\n",
       "Steak Soft Tacos                             56\n",
       "6 Pack Soft Drink                            55\n",
       "Chips and Tomatillo Red Chili Salsa          50\n",
       "Chicken Crispy Tacos                         50\n",
       "Chips and Tomatillo Green Chili Salsa        45\n",
       "Carnitas Soft Tacos                          40\n",
       "Steak Crispy Tacos                           36\n",
       "Chips and Tomatillo-Green Chili Salsa        33\n",
       "Steak Salad Bowl                             31\n",
       "Nantucket Nectar                             29\n",
       "Chips and Tomatillo-Red Chili Salsa          25\n",
       "Barbacoa Soft Tacos                          25\n",
       "Chips and Roasted Chili Corn Salsa           23\n",
       "Izze                                         20\n",
       "Veggie Salad Bowl                            18\n",
       "Chips and Roasted Chili-Corn Salsa           18\n",
       "Barbacoa Crispy Tacos                        12\n",
       "Barbacoa Salad Bowl                          10\n",
       "Chicken Salad                                 9\n",
       "Carnitas Crispy Tacos                         8\n",
       "Veggie Soft Tacos                             8\n",
       "Burrito                                       6\n",
       "Veggie Salad                                  6\n",
       "Carnitas Salad Bowl                           6\n",
       "Bowl                                          4\n",
       "Steak Salad                                   4\n",
       "Salad                                         2\n",
       "Crispy Tacos                                  2\n",
       "Chips and Mild Fresh Tomato Salsa             1\n",
       "Carnitas Salad                                1\n",
       "Veggie Crispy Tacos                           1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#被下单数最多商品(item)是什么?\n",
    "# print(chipo[['item_name','quantity']].groupby(by=['item_name']).describe())\n",
    "chipo[['item_name','quantity']].groupby(by=['item_name']).sum().sort_values(by = ['quantity'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在item_name这一列中，一共有多少种商品被下单？\n",
    "chipo['item_name'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "choice_description\n",
       "[Diet Coke]                                                                          134\n",
       "[Coke]                                                                               123\n",
       "[Sprite]                                                                              77\n",
       "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]]                42\n",
       "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]     40\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在choice_description中，下单次数最多的商品是什么？\n",
    "chipo[['quantity','choice_description']].groupby('choice_description').sum().sort_values('quantity',ascending=False)\n",
    "chipo['choice_description'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(4972)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#一共有多少商品被下单？\n",
    "chipo['quantity'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将item_price转换为浮点数\n",
    "#货币符号后取起\n",
    "\n",
    "# print(chipo['item_price'])\n",
    "#为什么从0开始不行？\n",
    "#报错：could not convert string to float: '$2.39 '\n",
    "chipo['item_price'] = chipo['item_price'].apply(lambda x:float(x[1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(39237.02)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在该数据集对应的时期内，收入(revenue)是多少？\n",
    "((chipo['quantity']*chipo['item_price'])).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#在该数据集对应的时期内，一共有多少订单？\n",
    "chipo['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    2,    3, ..., 1832, 1833, 1834], shape=(1834,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chipo['order_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_price_sum    21.394231\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每一单(order)对应的平均总价是多少？\n",
    "chipo['item_price_sum'] = chipo['quantity'] * chipo['item_price']\n",
    "chipo[['order_id','item_price_sum']].groupby('order_id').sum().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高级数据分析\n",
    "### 1. 商品类别分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "商品类别分析：\n",
      "           总数量      总销售额   订单数\n",
      "category                      \n",
      "Bowl      1398  15256.10  1331\n",
      "Burrito   1231  13180.94  1172\n",
      "Chips     1171   4985.86  1084\n",
      "Tacos      358   3628.27   345\n",
      "Beverage   792   1994.37   668\n",
      "Salad       22    191.48    22\n"
     ]
    }
   ],
   "source": [
    "# 提取商品主类别（Bowl, Burrito, Tacos等）\n",
    "def extract_category(item_name):\n",
    "    \"\"\"从商品名称中提取主类别\"\"\"\n",
    "    if 'Bowl' in item_name:\n",
    "        return 'Bowl'\n",
    "    elif 'Burrito' in item_name:\n",
    "        return 'Burrito'\n",
    "    elif 'Tacos' in item_name or 'Taco' in item_name:\n",
    "        return 'Tacos'\n",
    "    elif 'Salad' in item_name:\n",
    "        return 'Salad'\n",
    "    elif 'Chips' in item_name:\n",
    "        return 'Chips'\n",
    "    elif 'Drink' in item_name or 'Soda' in item_name or 'Water' in item_name or 'Izze' in item_name or 'Nectar' in item_name:\n",
    "        return 'Beverage'\n",
    "    else:\n",
    "        return 'Other'\n",
    "\n",
    "chipo['category'] = chipo['item_name'].apply(extract_category)\n",
    "\n",
    "# 按类别统计\n",
    "category_stats = chipo.groupby('category').agg({\n",
    "    'quantity': 'sum',\n",
    "    'item_price_sum': 'sum',\n",
    "    'order_id': 'count'\n",
    "}).round(2)\n",
    "\n",
    "category_stats.columns = ['总数量', '总销售额', '订单数']\n",
    "category_stats = category_stats.sort_values('总销售额', ascending=False)\n",
    "print('\\n商品类别分析：')\n",
    "print(category_stats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 订单价值分析（RFM模型的M部分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "订单价值分布：\n",
      "value_segment\n",
      "低价值(<$10)          0\n",
      "中等价值($10-20)    1207\n",
      "高价值($20-30)      375\n",
      "超高价值(>$30)       237\n",
      "Name: count, dtype: int64\n",
      "\n",
      "平均订单价值: $21.39\n",
      "中位数订单价值: $16.65\n"
     ]
    }
   ],
   "source": [
    "# 计算每个订单的总金额\n",
    "order_value = chipo.groupby('order_id')['item_price_sum'].sum().reset_index()\n",
    "order_value.columns = ['order_id', 'total_amount']\n",
    "\n",
    "# 订单价值分段\n",
    "order_value['value_segment'] = pd.cut(order_value['total_amount'], \n",
    "                                       bins=[0, 10, 20, 30, 100],\n",
    "                                       labels=['低价值(<$10)', '中等价值($10-20)', '高价值($20-30)', '超高价值(>$30)'])\n",
    "\n",
    "# 统计各价值段订单数\n",
    "value_distribution = order_value['value_segment'].value_counts().sort_index()\n",
    "print('\\n订单价值分布：')\n",
    "print(value_distribution)\n",
    "print(f'\\n平均订单价值: ${order_value[\"total_amount\"].mean():.2f}')\n",
    "print(f'中位数订单价值: ${order_value[\"total_amount\"].median():.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 商品组合分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "每单商品数量分析：\n",
      "平均每单商品数: 2.52\n",
      "最多商品数: 23\n",
      "最少商品数: 1\n",
      "\n",
      "商品数量分布：\n",
      "item_count\n",
      "1      128\n",
      "2     1012\n",
      "3      484\n",
      "4      134\n",
      "5       44\n",
      "6       14\n",
      "7        4\n",
      "8        5\n",
      "9        2\n",
      "10       1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 分析每个订单包含的商品数量\n",
    "items_per_order = chipo.groupby('order_id').size().reset_index(name='item_count')\n",
    "\n",
    "print('\\n每单商品数量分析：')\n",
    "print(f'平均每单商品数: {items_per_order[\"item_count\"].mean():.2f}')\n",
    "print(f'最多商品数: {items_per_order[\"item_count\"].max()}')\n",
    "print(f'最少商品数: {items_per_order[\"item_count\"].min()}')\n",
    "\n",
    "# 商品数量分布\n",
    "print('\\n商品数量分布：')\n",
    "print(items_per_order['item_count'].value_counts().sort_index().head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 蛋白质类型分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "蛋白质类型分析：\n",
      "          销售数量       销售额\n",
      "protein                 \n",
      "Chicken   1654  17742.15\n",
      "Steak      734   8072.62\n",
      "Veggie     217   2237.09\n",
      "Barbacoa   204   2062.68\n",
      "Carnitas   186   1994.25\n"
     ]
    }
   ],
   "source": [
    "# 提取蛋白质类型\n",
    "def extract_protein(item_name):\n",
    "    \"\"\"从商品名称中提取蛋白质类型\"\"\"\n",
    "    if 'Chicken' in item_name:\n",
    "        return 'Chicken'\n",
    "    elif 'Steak' in item_name:\n",
    "        return 'Steak'\n",
    "    elif 'Carnitas' in item_name:\n",
    "        return 'Carnitas'\n",
    "    elif 'Barbacoa' in item_name:\n",
    "        return 'Barbacoa'\n",
    "    elif 'Veggie' in item_name:\n",
    "        return 'Veggie'\n",
    "    else:\n",
    "        return 'None'\n",
    "\n",
    "chipo['protein'] = chipo['item_name'].apply(extract_protein)\n",
    "\n",
    "# 蛋白质类型统计（排除None）\n",
    "protein_stats = chipo[chipo['protein'] != 'None'].groupby('protein').agg({\n",
    "    'quantity': 'sum',\n",
    "    'item_price_sum': 'sum'\n",
    "}).round(2)\n",
    "\n",
    "protein_stats.columns = ['销售数量', '销售额']\n",
    "protein_stats = protein_stats.sort_values('销售额', ascending=False)\n",
    "print('\\n蛋白质类型分析：')\n",
    "print(protein_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.histplot(chipo['item_price'], bins=50, kde=True, color='steelblue')\n",
    "plt.xlabel('Price ($)')\n",
    "plt.ylabel('Ratio')\n",
    "plt.title('Item Price Distribution')\n",
    "# 去除极端值对图形的影响，显示到99百分位\n",
    "plt.xlim(0, chipo['item_price'].quantile(0.99))\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 数据可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 商品类别销售额对比\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# 1. 类别销售额饼图\n",
    "category_revenue = chipo.groupby('category')['item_price_sum'].sum().sort_values(ascending=False)\n",
    "axes[0, 0].pie(category_revenue, labels=category_revenue.index, autopct='%1.1f%%', startangle=90)\n",
    "axes[0, 0].set_title('Category Revenue Distribution')\n",
    "\n",
    "# 2. 蛋白质类型销售对比\n",
    "protein_revenue = chipo[chipo['protein'] != 'None'].groupby('protein')['item_price_sum'].sum().sort_values()\n",
    "axes[0, 1].barh(protein_revenue.index, protein_revenue.values, color='coral')\n",
    "axes[0, 1].set_xlabel('Revenue ($)')\n",
    "axes[0, 1].set_title('Protein Type Revenue')\n",
    "\n",
    "# 3. 订单价值分布箱线图\n",
    "order_totals = chipo.groupby('order_id')['item_price_sum'].sum()\n",
    "axes[1, 0].boxplot(order_totals, vert=True)\n",
    "axes[1, 0].set_ylabel('Order Total ($)')\n",
    "axes[1, 0].set_title('Order Value Distribution')\n",
    "axes[1, 0].grid(alpha=0.3)\n",
    "\n",
    "# 4. Top 10 商品销售额\n",
    "top_items = chipo.groupby('item_name')['item_price_sum'].sum().sort_values(ascending=False).head(10)\n",
    "axes[1, 1].barh(range(len(top_items)), top_items.values, color='steelblue')\n",
    "axes[1, 1].set_yticks(range(len(top_items)))\n",
    "axes[1, 1].set_yticklabels(top_items.index, fontsize=8)\n",
    "axes[1, 1].set_xlabel('Revenue ($)')\n",
    "axes[1, 1].set_title('Top 10 Items by Revenue')\n",
    "axes[1, 1].invert_yaxis()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 客户价值分析（简化RFM模型）\n",
    "由于数据中没有时间和客户ID，我们用订单ID模拟客户分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "订单价值分析：\n",
      "          Total_Quantity  Total_Amount  Item_Count Amount_Score Quantity_Score\n",
      "order_id                                                                      \n",
      "1                      4         11.56           4          Low            Low\n",
      "2                      2         33.96           1          VIP            Low\n",
      "3                      2         12.67           2          Low            Low\n",
      "4                      2         21.00           2         High            Low\n",
      "5                      2         13.70           2       Medium            Low\n",
      "6                      2         17.50           2         High            Low\n",
      "7                      2         15.70           2       Medium            Low\n",
      "8                      2         10.88           2          Low            Low\n",
      "9                      3         12.85           2       Medium            Low\n",
      "10                     2         13.20           2       Medium            Low\n",
      "\n",
      "按消费金额分组统计：\n",
      "             Total_Amount                 \n",
      "                    count   mean       sum\n",
      "Amount_Score                              \n",
      "Low                   462  11.56   5342.23\n",
      "Medium                456  13.94   6357.05\n",
      "High                  460  19.48   8959.05\n",
      "VIP                   456  40.74  18578.69\n",
      "\n",
      "按购买数量分组统计：\n",
      "               Total_Quantity             \n",
      "                        count   mean   sum\n",
      "Quantity_Score                            \n",
      "Low                      1778   2.51  4463\n",
      "Medium                     44   6.86   302\n",
      "High                        5  11.80    59\n",
      "VIP                         7  21.14   148\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s7/w2gl1bq96tnb0wq2g8344qy80000gn/T/ipykernel_77427/3529063441.py:25: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  print(order_analysis.groupby('Amount_Score').agg({\n",
      "/var/folders/s7/w2gl1bq96tnb0wq2g8344qy80000gn/T/ipykernel_77427/3529063441.py:30: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  print(order_analysis.groupby('Quantity_Score').agg({\n"
     ]
    }
   ],
   "source": [
    "# 计算每个订单的指标\n",
    "order_analysis = chipo.groupby('order_id').agg({\n",
    "    'quantity': 'sum',           # 总购买数量 (Frequency)\n",
    "    'item_price_sum': 'sum',     # 总消费金额 (Monetary)\n",
    "    'item_name': 'count'         # 商品种类数\n",
    "}).round(2)\n",
    "\n",
    "order_analysis.columns = ['Total_Quantity', 'Total_Amount', 'Item_Count']\n",
    "\n",
    "# 计算分位数进行分组（金额）\n",
    "order_analysis['Amount_Score'] = pd.qcut(order_analysis['Total_Amount'], \n",
    "                                          q=4, labels=['Low', 'Medium', 'High', 'VIP'])\n",
    "\n",
    "# 使用 cut 代替 qcut 来避免重复值问题\n",
    "quantity_bins = [0, 5, 10, 15, order_analysis['Total_Quantity'].max() + 1]\n",
    "quantity_labels = ['Low', 'Medium', 'High', 'VIP']\n",
    "order_analysis['Quantity_Score'] = pd.cut(order_analysis['Total_Quantity'], \n",
    "                                           bins=quantity_bins,\n",
    "                                           labels=quantity_labels)\n",
    "\n",
    "print('\\n订单价值分析：')\n",
    "print(order_analysis.head(10))\n",
    "\n",
    "print('\\n按消费金额分组统计：')\n",
    "print(order_analysis.groupby('Amount_Score').agg({\n",
    "    'Total_Amount': ['count', 'mean', 'sum']\n",
    "}).round(2))\n",
    "\n",
    "print('\\n按购买数量分组统计：')\n",
    "print(order_analysis.groupby('Quantity_Score').agg({\n",
    "    'Total_Quantity': ['count', 'mean', 'sum']\n",
    "}).round(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. 交叉分析 - 类别与蛋白质组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "类别与蛋白质交叉分析（销售额）：\n",
      "protein   Barbacoa  Carnitas  Chicken     None    Steak   Veggie\n",
      "category                                                        \n",
      "Beverage      0.00      0.00     0.00  1994.37     0.00     0.00\n",
      "Bowl        778.76    897.05  9550.88    74.00  2870.96  1084.45\n",
      "Burrito     894.75    616.33  6387.06    44.40  4236.13  1002.27\n",
      "Chips         0.00      0.00     0.00  4985.86     0.00     0.00\n",
      "Salad         0.00      8.99    81.09    14.80    35.66    50.94\n",
      "Tacos       389.17    471.88  1723.12    14.80   929.87    99.43\n"
     ]
    },
    {
     "data": {
      "image/png": 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SN8uM4zTLJ598YrummsxM6tQ39z2d+WBuxoSaYMUsZiye+4daEyzHnjLBfLA3XXFN4J6YzDkw06eYdsUw59lU5TQZIBOsGTHBfOzrZrKDpnKomZLFjIGN7cSJE/IGU7nVjAeMeY2Y15bJCL722mvX7Wueb+znZb4EMNlqc73N1DixvxQwlXXNa94EM7EzruZ+7OkybiWYixl7bJg2my963JlxpGbfN998034pk9jn/FYez5yT2OfDvJZiZ2NjvtyJ6+9AfMT1d8hw7+ocW+y/G6a9hhmbCwC4PWQyAdyQ+WD5xRdf2A/YZoyeyeaZKTciIiJsdzST6TFFdYyGDRva7rGmq6HJHpkPo+YDvglCYgcf5kOrmRLDjJ0zXSnNPqagh+neZjKhJotkjmv2M2MmN23aZDMkZoyUyT6a7qcmUDMZP5N1MN0jze+ZsVixsySmi5/5sGgysWYuSDNmzHyINN343Od5vBnm+ZtuocbNdJWNYbJ8JigxY8TM8zEf1t3t3LnTdjU2gZApeGKCV9O91gT27lNmJAZT/MUEiOZ8m3GHJntjzrXp0ms+mMeMOTXZP9M2E4yazJ0Jms1rwSzmQ7qZU9QU3THFmEx207TdfGFgphIxQVpSM9fctM1kIk1hFxMsm9el6YJsCtmY16u5LiaLZV7H5ssSM12Ne/Btnru53iZwiV3gybw3zOvXTK9jXpfmNWn2N9OfmGtnzmvMa+VmmSltzHhcc0zzWjbn2LxmYr5ciWEyzib4Nc/R/I4pcmTGvJoxkCbDbzKOJuOeUG7l8cx71xReMu8v83oxrwGTQXafSscwX/6Y82rGIpsvo0y38pg5duPDtMV0HTdzkJovokw7zRdKMfPFxsVsa9mype1Oa9preg6Y7vFmuhkAwG26zaq0APyImVKka9euzsKFCzvTpEnjzJgxo/O+++5zTpw40WPaijlz5jgrVqzoDA4OtvuOHj3a+emnn143LcPRo0edzZo1s8cx29ynM7lw4YJzwIABzuLFi9vHyp49u50K480337RTa8Qw0ys89thj9hiZM2d2durUybls2TJ7vK+++sqj/YsWLbLtNdMfZMqUydmiRQvn1q1bPfaJmc4gZtqGuJipR8y0FSVLlrzlc7hw4UJ7fDMNxsGDBz22nTx50k63Ubp0aTuFi3k+9957r/Prr7/+z+PeTLsNc47N1DFxOXbsmLNz5872XJtzbqYjiWtqFjOlRJUqVew+sacz2b17t50SxkzLERgY6MyXL5+zefPmzpkzZ/7nFCZxtatjx452Kov/YvYxr6W4TJ069bppZj766CP7HMxrwbx2zHPt16+f8/Dhw9f9fvv27e3vN2jQ4IaPb6bTqVGjhr1uZjHX0FxLM6XM7TxHcx7N48VMQfPKK6+4Xjuxp/tYt26ds02bNs5s2bLZ/c2xHn74YefixYtvagqTG02Zc6PX1M08npmOJ+a1ZKbAMdOebN++3e5rnq+7jz/+2E5vZN5T7s/vRtc09rQ0N3ouhw4dcrZu3doZEhJi30sPPfSQvb6xX7Mxz9P8LXjwwQft6yFLlizOnj17OkNDQ//1HAIA/p3D/HO7ASoAJCemaE7r1q1tdUwz9i6hmakQTBdcM9brRpVUAfgG05PBdHc23X3/q2gWAODWMCYTgE8y3V7dmTFfphus6S535513JspjTp061T5O7HkEAQAAcA1jMgH4JDNVggk0zVhLUxzn22+/teNETUXRhJ6qw0wjsnXrVr3++ut27B1VJwEAAG6MIBOATzJFQsw0CT/++KOtzGoKCJlMZs+ePRP8sYYPH24DWNMFN6byJAAAAOLGmEwAAAAAQIJhTCYAAAAAIMEQZAIAAAAAEgxBJgAAAAAgwaTQwj9rvN0AAEA8OffP5xz6EUehxt5uAoBEUcUnz+swR6lEf4whzh1KqchkAgAAAAASTArNZAIAAADA7SETFz+cPwAAAABAgiGTCQAAAABuyMTFD+cPAAAAAJBgyGQCAAAAgBsycfHD+QMAAAAAJBgymQAAAADghkxc/HD+AAAAAAAJhkwmAAAAALghExc/nD8AAAAAQIIhkwkAAAAAbhycjZSRyfzf//6n++67T3nz5tX+/fvtuvHjx+v777/3dtMAAAAAAL4UZH7wwQfq06ePmjZtqrNnzyoyMtKuDwkJsYEmAAAAACRlkJTYS0qWLJ7fxIkT9fHHH+vVV19VqlSpXOvvuusubdq0yattAwAAAAD42JjMvXv3qnLlytetDwoK0qVLl7zSJgAAAAD+KVlk4nxYsjh/RYoU0fr1669bP3/+fJUpU8YrbQIAAAAA+Ggm04zH7NGjh8LCwuR0OrVq1Sp9+eWXGjlypD755BNvNw8AAACAH0kWmTgfliyCzKeeekpp06bVwIEDdfnyZT322GO2yuyECRPUrl07bzcPAAAAAOBLQabRvn17u5gg8+LFi8qZM6e3mwQAAADAD5HJTCFBZox06dLZBQAAAADge5JFkGkqyzocjuvWm3XBwcEqXry4OnXqpLp163qlfQAAAAD8B5nMFHD+GjdurD179ih9+vQ2kDRLhgwZtHv3bt199906cuSIGjRooO+//97bTQUAAAAAJPdM5smTJ9W3b18NGjTIY/2IESO0f/9+LViwQEOGDNFrr72mBx54wGvtBAAAAJDyJYtMnA9LFufv66+/1qOPPnrdelNZ1mwzzPYdO3Z4oXUAAAAAAJ8KMs24y+XLl1+33qwz24yoqCjXbQAAAABIzCApsZeULFl0l+3Vq5e6deumNWvW2DGYxurVq/XJJ5/olVdesfd//vlnVapUycstBQAAAAD8G4fT6XQqGZg+fbreffddV5fYUqVK2eDzscces/dDQ0Nd1Wb/25pEbi0AILE598/nJPsRR6HG3m4CgERRxSfP6yeOUon+GE85U+5QwGSRyTTat29vlxtJmzZtkrYHAAAAAODDQSYAAAAAJAcpfcykXwSZkZGRevvtt20l2QMHDigiIsJj++nTp73WNgAAAACAjwXpw4YN07hx4/TII4/o3Llz6tOnj9q0aaOAgAANHTrU280DAAAA4EeoLpsCgkxT9Ofjjz9W3759lTp1ajsnpqksO3jwYK1cudLbzQMAAAAA+FKQefToUVWoUMHezpAhg81mGs2bN9fcuXO93DoAAAAA/oRMZgoIMvPnz68jR47Y28WKFdOCBQtcc2UGBQV5uXUAAAAAAJ8KMlu3bq3Fixfb22ZuzEGDBqlEiRLq0KGDnnzySW83zydMn75A9eo9pwoVOuqhhwZp48a/vd0kJBKutX/hevue1RuPqdugJarZ7huVbvi5Fi074LH95JlQ9R+7zG6v1GK6nnplkfb9c95jnwOHL6jn0F9U7aEZqtLqS70w4jf7e3GJiIhUq24/2MfatptCeb6C97b/4Fr7JkcSLClZsggyR40apVdeecXeNsV/li5dqu7du2vmzJl2G/7dvHkrNHLkNPXo0Ubfffe6SpcuqC5dRunUqehux0g5uNb+hevtm0LDrqp00Swa3PPe67Y5nU71GPqLDh25oPeH1dW37zdX3pzp9eTLC3U59Irdx/zsMmChHA6Hpo5pqC/ebqwrV6LUffASRUU5rzvm2E/WKGe2dEny3JAweG/7D641/JXXg8wrV67YbOXevXtd66pWrWorzLZo0cKrbfMVU6bM08MP11XbtnVUvHh+DRvWRcHBQZo16zdvNw0JjGvtX7jevqnWPfn0QufKur9Gweu27fvngjZsO6khz1VVhVLZVbRAZg19rqrCwiM199d9dp+1W07on2OXNPLF6ipVJItdRvW7T5t3ntLK9dFDS2L8vuofLVtzRP2erpJkzw/xx3vbf3CtfRdjMn08yAwMDNSsWbO83QyfFRFxVVu27FX16uVd68zUL+b+unW7vNo2JCyutX/heqdMEVci7c+gNKlc6wICHEoTGKA1m4+79jHdqNIEXtsnKDCVAhwO1z6G6T47aPwKjX75PgUHJYtpr3ETeG/7D641/JnXg0yjVatWmj17treb4ZPOnLmgyMgoZcuW2WO9uX/y5FmvtQsJj2vtX7jeKZPJXJruseM+XatzF8JtQPnxjM06evKyTpy+bPepVCaH0gan1puT19qut6b77OiP/1JklFMnToe6ut0OGLtM7ZqVVIWS2b38rHAreG/7D661byOTGT/J4qtPU+Rn+PDhWrZsmapUqaL06dN7bH/uuedu+Lvh4eF2cRcUFKGgoDSJ1l4AAG5HYOoAvTO4jgaOW657285QqgCHqt2ZR7XuzmcDRyNrSLDGD6ytYRNX6n+zt9kMZrO6RVS2eFab9TT+N3u7LoVe0dPtrvViAQAguUgWQebkyZMVEhKiNWvW2MWdKXzwb0HmyJEjNWzYMI91Q4Z01dChz8gfZMmSUalSBVxX5Mfcz549xGvtQsLjWvsXrnfKVb5kNs2e1EIXLkXYgj4mqHy41zy7PkaNu/Jq4WdtdOZcmP0bnylDGtV45GsVyJ3Bbv9z/VGt33ZSFZtN9zj2gz3mqnm9Ihrdr0aSPy/cHN7b/oNr7duSRXdPH5Yszp8p+nOjZc+ePf/6uwMGDNC5c+c8lgEDOstfpEmTWuXKFdGKFVtc66Kiouz9ypVLeLVtSFhca//C9U75MqZPYwNMM33J5l2nVK9agev2yZI52AaYK9cd0amzYar7//u82uNuzf6gub77/+XDEfXt+nGv1lLvzpWT/Lng5vHe9h9ca/izZJHJjBEREWEDy2LFiil16ptrWlBQkF08+VdX2c6dm+rllyepfPmiqlixmD777CeFhoapTZva3m4aEhjX2r9wvX2T6cZq5rmMcejoRTt/ZeaMaZQ3ZwbN/32fDR7N2Myde8/o9Q9Wq371AjZ7GWPWz3+rWMHMypo5WOu3ntDrH6xSxzZl7ZhOwxzHXbq0gfZnwbwZlTuH55ATJD+8t/0H19p3JYtMnA9LFkHm5cuX1atXL3322Wf2/s6dO1W0aFG7Ll++fOrfv7+3m5isNW1aTadPn9c778zUiRNnVaZMIX3ySX9lz+5ZDAi+j2vtX7jevslMNdLxpQWu+6M+/Mv+bHV/MY166T4dPxWqUZP+spnJHFnT6oEGRdW9fUWPY+w7dE5v2+JAEcqbK726PVpRndqWSfLngsTBe9t/cK3hrxzOmEoDXvT888/boj/jx49X48aNtXHjRhtkfv/99xo6dKjWrVt3i0f0HNcJAPA9zv3zvd0EJCFHocacbyBF8s15fGc5SiX6Y7R17lBKlSwymWb6khkzZqhq1aq20E+McuXKaffu3V5tGwAAAADAx4LMEydOKGfOnNetv3TpkkfQCQAAAACJjTGZKeD83XXXXZo7d67rfkxg+cknn6hatWpebBkAAAAAwOcymW+88YaaNGmirVu36urVq5owYYK9vXz5cv3222/ebh4AAAAAP5IsMnE+LFmcvxo1amj9+vU2wKxQoYIWLFhgu8+uWLFCVar45mBhAAAAAPBHySKTaZi5MT/++GNvNwMAAACAn0sWmTgflizOX4MGDTR16lSdP3/e200BAAAAAPh6kGmmKhkwYIBy586thx56yM6PeeXKFW83CwAAAICfBkmJvaRkyeL5mUI///zzj50vM3369OrQoYNy5cqlp59+msI/AAAAAOBDkkWQaQQEBKhhw4a22+yxY8f04YcfatWqVapXr563mwYAAADAj5DJTCGFf2IcPXpUX331laZNm6aNGzfqnnvu8XaTAAAAAPgRh7cb4OOSRSbTFPyZMmWK7r//fhUoUEAffPCBWrZsqV27dmnlypXebh4AAAAAwJcymWb8ZZYsWfTII49o5MiRuuuuu7zdJAAAAAB+Kllk4nxYsggy58yZo/r169txmQAAAAAA35UsojrTTTYqKkqLFi2yBX8uXLhg1x8+fFgXL170dvMAAAAA+BEK/6SATOb+/fvVuHFjHThwQOHh4TbozJgxo0aPHm3vT5o0ydtNBAAAAAD4Sibz+eeft+Mwz5w5o7Rp07rWt27dWosXL/Zq2wAAAAD4FzKZKSCTuXTpUi1fvlxp0qTxWF+4cGH9888/XmsXAAAAAMAHg0wzHjMyMvK69YcOHbLdZgEAAAAgqTiYKNP3u8s2bNhQ48ePd913OBy24M+QIUPUtGlTr7YNAAAAAOBjmcy33npLjRo1UtmyZRUWFqbHHntMu3btUrZs2fTll196u3kAAAAA/EiAw+ntJvi0ZBFk5s+fXxs2bNBXX32ljRs32ixmly5d1L59e49CQAAAAACA5C1ZdJc9deqUUqdOrccff1y9evVS9uzZtWPHDv3111/ebhoAAAAAPxyTmdhLSubVIHPTpk22gmzOnDlVunRprV+/XnfffbfefvttffTRR6pbt65mz57tzSYCAAAAAHwlyOzXr58qVKig33//XXXq1FHz5s3VrFkznTt3zs6Z+cwzz2jUqFHebCIAAAAAP+NIgiUlczidTq+NajXdYpcsWaKKFSvacZiZMmXS6tWrVaVKFbt9+/btqlq1qs6ePXuLR16TKO0FACQd5/75nG4/4ijU2NtNAJAooj/X+5qlqUsm+mPUvLpTKZVXC/+cPn1auXPntrczZMig9OnTK0uWLK7t5vaFCxe82EIAAAAA/sZBdVnfLvxj5sT8t/sAAAAAAN/h9SlMOnXqpKCgIHvbzJHZrVs3m9E0wsPDvdw6AAAAAP6GvJcPB5kdO3b0uG+mMImtQ4cOSdgiAAAAAIDPBplTpkzx5sMDAAAAwHXIZPr4mEwAAAAAwI1FRkZq0KBBKlKkiNKmTatixYrptddek/tEIeb24MGDlSdPHrtPgwYNtGvXrusKr7Zv397O6hESEqIuXbrYWT7cbdy4UTVr1lRwcLAKFCigMWPG6FYRZAIAAACAe5DkcCb6citGjx6tDz74QO+++662bdtm75vgb+LEia59zP133nlHkyZN0p9//mnr3DRq1MjWvYlhAswtW7Zo4cKF+vHHH/X777/r6aefdm0/f/68GjZsqEKFCmnNmjUaO3ashg4dqo8++sh35slMPMyTCQC+jnky/QvzZAIplW/Ok7kyuHiiP0bVsL9vet/mzZsrV65cmjx5smtd27ZtbcZy2rRpNouZN29e9e3bVy+++KLdfu7cOfs7U6dOVbt27WxwWrZsWa1evVp33XWX3Wf+/Plq2rSpDh06ZH/fBLKvvvqqjh49qjRp0th9+vfvr9mzZ2v79u033V4ymQAAAADgxpEES3h4uM0cui83ml2jevXqWrx4sXbu3Gnvb9iwQX/88YeaNGli7+/du9cGhqaLbIzMmTPr3nvv1YoVK+x989N0kY0JMA2zf0BAgM18xuxTq1YtV4BpmGzojh07dObMGYJMAAAAAEiuRo4caQNB98Wsi4vJJppsZOnSpRUYGKjKlSvrhRdesN1fDRNgGiZz6c7cj9lmfubMmdNje+rUqZU1a1aPfeI6hvtj+MQ8mQAAAADgb9VlBwwYoD59+nisCwoKinPfr7/+WtOnT9cXX3yhcuXKaf369TbINF1cY08LmRykzCDz0iFvtwBJKfIK59tPDMv8qrebgCQ0JGo659uPOFffWlEJ+C5HiVrebgKSUohvjslMCkFBQTcMKmN76aWXXNlMo0KFCtq/f7/NfJogM3fu3Hb9sWPHbHXZGOZ+pUqV7G2zz/Hjxz2Oe/XqVVtxNub3zU/zO+5i7sfsczMYkwkAAAAAsTKZib3cisuXL9uxk+5SpUqlqKgoe9tMbWKCQDNuM4YZ42nGWlarVs3eNz/Pnj1rq8bGWLJkiT2GGbsZs4+pOHvlyrUkjqlEW6pUKWXJkuWm20uQCQAAAADJWIsWLfT6669r7ty52rdvn7777juNGzdOrVu3ttsdDoftPjtixAjNmTNHmzZtUocOHWx32latWtl9ypQpo8aNG6tr165atWqVli1bpp49e9rsqNnPeOyxx2zRHzN/ppnqZMaMGZowYcJ13Xr9s7ssAAAAANwmxy3OY5nYJk6cqEGDBunZZ5+1XV5NUPjMM89o8ODBrn369eunS5cu2XkvTcayRo0adoqS4OBg1z5mXKcJLOvXr28zo2YaFDO3ZgxTfGjBggXq0aOHqlSpouzZs9vHcJ9L03/nybz0vbdbgKTEmEy/wZhM/8KYTP/i/OtjbzcBSYQxmX4mJLr6qa/5K32xRH+Muy7tVkpFJhMAAAAA3AQkQXXZlIwxmQAAAACABEMmEwAAAACSeJ7MlIxMJgAAAAAgwZDJBAAAAAA3DqW82qhJiUwmAAAAACDBkMkEAAAAADeMyYwfMpkAAAAAgARDJhMAAAAA3JDJjB8ymQAAAACABEMmEwAAAADcBDioLhsfZDIBAAAAAAmGTCYAAAAAuGFMZvyQyQQAAAAAJBgymQAAAADgxsHZiBcymQAAAACABEMmEwAAAADcOKguGy9kMgEAAAAACYZMJgAAAAC4obps/JDJBAAAAAAkGDKZAAAAAOAmgPKy8UImEwAAAACQYMhkAgAAAIAbqsvGD5lMAAAAAIDvZzLPnz9/0/tmypQpUdsCAAAAADEYkumjQWZISIgc/1Eb2Ol02n0iIyOTrF0AAAAAAB8MMn/55RdvPTQAAAAA3BDzZPpokFm7dm1vPTQAAAAAICVXl61Vq5bq1KljA8/77rtPwcHB3m4SAAAAAD9FddkUUF22YcOGWrlypR544AE7VrNGjRoaOHCgFi5cqMuXL3u7eQAAAAAAX8pkmoDSuHr1qlavXq3ffvtNv/76q8aMGaOAgACFhYV5u4kAAAAA/EQA5WV9P8iMsWfPHm3atEkbNmzQxo0blTFjRtuV1l/VazZS/xw5c936xx6qpi4da6t+81Fx/t740Y+ryf0V7e0Vf+7ShA8WaMffR5QubRq1an6XevdopNSpUyV6+3HzIiOjNPGjxZozf4NOnrqgnNkzqXXzynq2S11XFeaTpy7qzYnz9ceff+vChTDdVbmwBr3UXIULZrfbDx0+o/oPvBnn8cePbKcmDSpwSbwkTYb0qvva8yrduoHS58ymo+u2av7zb+jwX5vs9gemjFSlTm08fufv+Us1vclTrvvP712skML5PfZZ1P9NLRv9sb2dKiiNmk8apjxVyilHmWLa+eOvmtG6R5I8P9yaevWe1z+HT163/rHHGmjI4M4eFda7Pj1GS5du1Hvv9laDBndxqpOZD+fs18LVJ7XnyGUFpwlQ5RKZ1PeRYiqaN51rnxNnwzX2yz1avvm0LoVFqkjudHrmgUJqdE8Ou/3PrWfU8Y0NcR7/m2F3qkKxTAqPiNSQKTu1Ze9F7Tl8SXUqZ9N7vfmbntRWr9uvydOWa/P2Izpx8qLeG/OwGtQu7fGefeejX/XN9+t0/mKY7qxYQEP7NVXhgtlc++w9cEpj3lmotRsP6sqVSJUqnkvPP1NHVe8qct3jnTl3WQ+0/1DHTlzQ6kX9lCkjw8ngO5JFkPnYY4/Z7GV4eLgNKs3YzP79+6tixYr/Oc1JSjZzWi9FRjpd93ftPqrO3T9W4/srKk+uEP2xYJDH/jO+XanJn/+mWveVsve37zysrs99qm5d6mn08Ed07MQ5DXn9O0VFRenl3s2T/Pngxj7+/Hd9OWuVRg9tq+JFc2nztn80YPgsZcwQrA7tqtv/uHq8NM1+OfD+m48rQ/ogTf1imTr3mKK5Xz9vv0DIkyuz/vipv8dxZ3y3WpOnLVWt6iU5/V7U4pMRylm+hL57op8uHD6uio+31BOLpuj9sk3tfWPXT7/r+84DXL8TGR5x3XF+GTRBaz7+2nU/4sIl1+2AVKl0NTRcq975n8q0bZTozwm3b+bM1+wXSzF27Tqkzk+OVONG93rs99ln8/36/0BfsHrbWT12f15VKJrJ/n/99td79NToDfpx9D1KFxz9Ze7Lk7brwuWrer9PBWXJGKgflx9T74lbNPO1KipbOKMql8yspe9W8zjuOzP3acWWMypfNKO9b14uwWlS6YlG+bRg1QmvPFdIl0MjVKpELrVtUVk9X772tzjGx/9brv99vUqjBrdS/rwhmvDhL+ry/HTN++pZBQVFf+Tu1udLFSqQVZ+910HBQan12Vd/qlvfr7Tw217KkS2Dx/FeHfGDDUJNkImkx5/fFDAm86uvvtKVK1f01FNPqVu3buratavuuOMOv//PNWuWDMqRPaNr+eX3bSqYP5vuqVJUqVIFeGwzy6JftqjJ/Xcofboge17n/bxBpUrkUc+n71ehgtl1T5Vieun5ppr+9XJdvEQX5ORk3cYDql+7jOrUKK38ebOocf3yqnFvCW3ccshu33fglNZvOqihL7dUxXL5VbRwDg3t31Jh4Vc09+fob8DjfE38utVmMGNeE0h6qYODVLZtQy3qN1YHlv6lM7sP6Ldh7+r03/t1V/fHPILKS8dOupaws+evO1b4hUse+1y5HOraZm7PfXao1n7yjS4e5UNocpY1ayblyBHiWn75dZ0KFsyle+4p49pn27Z9+nTKXL3x+tNebSv+3Scv36E2tfKoRP70Kl0og0Y+U1qHT4Vry75rQcH6Xef0eMN8qlgskwrkTKvurQorY/rU2rI3ep80qQOUIyTItYRkCNTitSfVplZu1+cgE7AO7VxSD9fNq+whabgsXlK7egn17lZP99e5lr2MYb4M/vyrP9W9c001qF1KpUvk0pihrXT85AUt+m273ef02cvad/C0nu5wn91uMpx9e9RXaNgV7dod/YVjjC9m/aULF8P05OOeX0Ag6Zi3X2IvKVmyCDJPnTqlTz75RBERERowYICyZ8+u6tWr65VXXtGCBQu83bxkIeLKVc35aa3aPnB3nMH35q2HtG3HYT3Y6m6P3wlK45msDg4OVHj4VW3Z9k+StBs3p3LFglq5erf27o/uQrd95xGt2bDPlYE019KI+SbUMOOV0wSm1pr1++M8psmGbtt5RA+2rMJl8KKA1KntcjUs3GO9yToWrHGn637hOvfoxWPL1WP7fDV7f6jSZg257lg1+nfVSydX6um136n6i13kSEW3d18XEXFVc+b8obZtarv+toeGhqvvi+9p8OBONgiF7zAZSyNz+mt/qyuVyKx5K4/r7MUriopyau6KY4q4EqV7ysR9bZesPaWzF67YIBO+49Dhszpx6qKq31PUtc70RrqjXD6t2xT9hXGWzGlVpFA2zf5po82KXr0apRnfrVG2LOlVrnQe1+/9veeE3p/8u0YPaaWAlB6JIMVKFt1ls2TJopYtW9rF+PvvvzVixAiNHTtWo0ePVmRkpPydyVKacXitbxAwzPx+tYoVyak77yjsWlejWil99sUf+nH+OpvhNGP93vtokd124uT1WRJ4z9Mda+nixXA1eWi8UgU4FBnlVO/u96tlk0p2u8lc5s0dorfeW6DhA1opbdpATf1iuY4eP6cTp+LuRjPz+79UrEgO3XlHoSR+NnAXcfGSDi5fq1qDntWJbXtsBrL8o82Vv1olnf77gGv85bZvF+rs3kPKUqyA6r/RR+1/+liTqz0iZ1R0t8o/3/mfjqzdqtDT51SgemXVH9lHGfLk0IK+cY/Nhm9YtPgvXbhwWa1bX6s/MHLkNFWuXFIN6jMG05eYAPKNaX/rzpKZVLLAtW6P43uVVe93t6pqt2VKncphx25OfKG8CuW+Nm7T3azfjqhGxazKnY3xd77EBJhGtqzpPdZny5pBJ09HbzNfJE2d+ISe7TdDd9YdpYAAh7JmSa9PJjymzJnSur546jPoW73Uq4Hy5s6sg/9cX5sDScOha0PW4KNBpslkxlSUNcvWrVvtVCYtWrSw4zP/jRnHaRZ3QVevKCgoUCnJrNmrVat6KeXKkfm6bWFhV/TjT+v0bNf6HutrVCupfi8005A3vlO/QTOUJjCVnu3aQH+t28s3Y8nMT4s264f5G/TWiIdVvGhOm4EcOW6ucubIqNbN71Rg6lSaOOYxvfrat7qn/gjbNbba3cVsptN00YnzNfHzRls4CN5nxmK2/PQN9T28VFFXr9pgcfOXc22RHmPLjHmufY9v3qljG3fo+T2LbXZz75KVdv3Kt6de22fTDkVGXFHzD4dp8YC37G34plkzf1WtmncoV64s9v7iJWu08s8t+u7bN7zdNNyi4Z/t0q5Dl/TFoMoe6yfM3GcznFP632HHZC5ac9KOyZw2qLJKuQWjxtFTYfpj42m93Sv6bwNSFvP/9bCx82zmcvqHnRQcFKhv5qyzYzJnTn1KObNn1FvvL1axwtn1QJPoAo6Ar0oWQWbOnDltF9maNWva8Zh16tRRhQo3VzVt5MiRGjZsmMe6IQMe0dBXH1VK8c/hM1q+apcmvtkhzu3zF220QUWr5tdnOTs/Xkud2tfU8ZPnlTljOv1z+LTemviT8ue/VukM3jdmwnybzWzWMPo/lVLFc+vwkbP6cOpvNsg0ypfJp++/6GXHaJiKdObbz4c6fWDXxzZ/yebo10Qzzw878I4zew7qszpPKDBdWgVlymDHTLb96m27Pi4mo3npxGllLV7IFWTG9s+fG5QqMNBWnD21c28iPwMkhn/+OaHlKzZr4sQXXOtWrtyqAweO6+57unrs2+u58bqrSmn973/RU34heRn+2U79uu6Upg2s5JGBPHAsVNMX/qMfRt1tx20aZuzmmh1n9cXCfzTsyehCfTG+/f2oQjIGqt6d/B/ta2KK9pw6fckGizFOnb6o0iWiuz6v/Guvfl22S6sX9lOGDNG1Ekw32eV/7tHsuRv0dMcaWvnXPu3cfVw/V99qt8d8j1y10Vh161RTzz1dJ+mfnJ+ip3IKCDLNdCXlyt3et3ZmDGefPn081gVdTVnjOL+ds9p2tzBFYeIy6/vVqle7rC0UFBfTPSMmA/rjz+uVJ3eIypW+PjCB94SFR8gRa0KmVAEBcWYpzRgPY9+Bk3bc5fPdGly3z6zv16herdI2EEXyYYrzmCU4JJOKN6qhhf3Gxrlfxny5lC5biC4cuXEBn9yVyigqMlKXjp9KxBYjMX377e/Kli2z6tS+9mXQ011b6KEHPT9EtmjZXwP6P6669a6N4UXyYP5Gv/b5Li3666Q+f7WS8ueM7vIYIzQiMs759kw3ySjn9ccyQeYDNXIpMHWyKJmBW2CqyZpAc8XqvSpTMjqoNMNgNmz5R4+2ie76bgr8GLH/vzf3o/7///uJox5SWHj02F5j09bDemXEHJv5LJgvK9cEPiNZBJkxAeaJEye0Y8cOe7tUqVLKkSN6Dql/ExQUZBcPl1JOV1kz3ci3c/6yWcq45rbcf+CkVq/dq4/eeTLO3//ks19Vs3op+x/agiWb9fGUXzV+dHvb3RLJR90apTVpyq92/IWZwsQUcZryxR9q6zYG96dFm2zQmDdXiHbsPqo33pqrBrXLqkbVEh7H2n/wlFav26ePxsed+UbSK9awhv1K9NSOvcpavKDuH9tPJ7fv0fop3yowfTrVGdJTW2f9rItHTyprsQJqMOYlW312989L7e/nr1pJ+e69Q/t+WWkrzBaoVlmN3h6gjdPmeFShzV6mmFKlCbRFg9JkTK9cd0R/MXVsQ3RlQySzv+3f/aZWrWp6/G2PqTgbW9682VUgf84kbiX+y/Cpu/TjimN2zsr0wansnJhGxnSp7ZQjRfOkU6FcaTXk053q91gxWznWdJddvvmMJvX17LG1cstZHToRpofqXCsA4+7vfy7pytUonbt41c63uW1/9Hj8MoWuZc2QuC5djtCBQ6c9iv1s23nUjqc0/393aHevPpiy1E5REj2Fya82qxkzl2alCgXsXJf9h81Wjy61FBQcqK9nr7U91upUj/6/vGB+z0DyzNnL9mexwjmYJzOJxf4yAD4YZF66dEm9evXS559/bv/jNVKlSqUOHTpo4sSJSpcu7sHx/mD5n3/r8NGztqrsjbKYuXNlVo1qnoFGjN+X7dCkyUtsddLSJfLqvbc7qvZ9cWdE4T0DX2qhCZMWadjoH3TqzEXlzJ5Jj7S5Rz2eujam8sTJCxr19k+2642ZnuSBppX0rNv2GLPmrFHunJlUo2rxJH4WuJGgzBltoZ5M+XMr9PRZbZu1QEtefduOzwxInUo5K5bUHR1bKTgko503c/eCZXZOzJixllfDI1S+XVPVGdpTqYLS2O60ZozminFTPB6n/byPbPfZGN3Wf29/DnN4dsmD9y1fvlmHD5+yVWXhu75cfNj+7PD6eo/1bzxdyk5tYjKSH75UQW/N2KPub23S5fBIFcyVVqOeKa3alTy7xM787Ygql8ikonnj7oHy9NiNOnzyWg2K1q+usT+3T6P7ZFLZvO2wOjz7uev+yPHRPedaN7tDowY/oK5PVFdoaIQGj/xR5y+GqcodBfXJhPauyvBZQ9LZ++M/WKKOPf6nK1cjVaJoDr039hGV/v/sJ5BSOJxx9cdLYs8884wWLVqkd999V/fdd59d98cff+i5557T/fffrw8++ODWDngp+oMV/EQkRU/8xbDMr3q7CUhCQ6Kmc779iPOvj73dBCQRR4lr1ZThB0LayxcdLlQw0R8j7/7oKvMpUbLIZM6aNUszZ860BX9iNG3aVGnTptXDDz9860EmAAAAAMB/g8zLly8rV65ccVadNdsAAAAAIKlQXTZ+kkX1l2rVqmnIkCEKCwtzrQsNDbVTk5htAAAAAADfkCwymRMmTFCjRo2UP39+3XHHHXbdhg0bbNXYBQtS1nQkAAAAAJI5qsv6fpBZvnx57dq1S9OnT9f27dGl9h999FG1b9/ejssEAAAAAPiGZNFd9tSpU3aakq5du+r5559X+vTp7XyZf/31l7ebBgAAAMDPOAISf0nJvPr0Nm3apMKFC9sCP6VLl9b69et1zz336O2339ZHH32kunXravbs2d5sIgAAAADAV4LMfv36qUKFCvr999/t9CXNmzdXs2bNdO7cOZ05c8bOnzlq1ChvNhEAAACAn3E4HIm+pGReHZO5evVqLVmyRBUrVrQFf0z28tlnn1VAQHTs26tXL1WtWtWbTQQAAAAA+EqQefr0aeXOndvezpAhgx2LmSVLFtd2c/vChQtebCEAAAAAf5PSx0wmNq+fvtip4pSeOgYAAACAlMzrU5h06tTJzodphIWFqVu3bjajaYSHh3u5dQAAAAD8Dokv3w0yO3bs6HH/8ccfv26fDh06JGGLAAAAAAA+G2ROmTLFmw8PAAAAANdhTKaPj8kEAAAAAKQcXh+TCQAAAADJiSOAYqTxQSYTAAAAAJBgyGQCAAAAgBuKy8YPmUwAAAAAQIIhkwkAAAAAbqguGz9kMgEAAAAACYZMJgAAAAC4o7psvJDJBAAAAAAkGDKZAAAAAOCG6rLxQyYTAAAAAJBgyGQCAAAAgBsHYzLjhUwmAAAAACDBkMkEAAAAADfMkxk/ZDIBAAAAAAmGTCYAAAAAuHFQXjZeyGQCAAAAABIMmUwAAAAAcEcqLl44fQAAAACABEMmEwAAAADcMCQzfshkAgAAAAASDJlMAAAAAHDjCHBwPuKBTCYAAAAAIMGkzExm1FVvtwBJKTA959tP9L3P6e0mAEgkzs27Obd+wlGynrebAPwnB6m4eOH0AQAAAAASTMrMZAIAAADA7aK8bLyQyQQAAAAAJBgymQAAAADghjGZ8UMmEwAAAACQYMhkAgAAAIAb5smMHzKZAAAAAIAEQyYTAAAAANxQXDZ+yGQCAAAAABIMmUwAAAAAcMOYzPghkwkAAAAASDBkMgEAAADAnYPTER9kMgEAAAAACYZMJgAAAAC4cZCKixdOHwAAAAAkc//8848ef/xxZcuWTWnTplWFChX0119/ubY7nU4NHjxYefLksdsbNGigXbt2eRzj9OnTat++vTJlyqSQkBB16dJFFy9e9Nhn48aNqlmzpoKDg1WgQAGNGTPmlttKkAkAAAAAsarLJvZyK86cOaP77rtPgYGB+umnn7R161a99dZbypIli2sfEwy+8847mjRpkv7880+lT59ejRo1UlhYmGsfE2Bu2bJFCxcu1I8//qjff/9dTz/9tGv7+fPn1bBhQxUqVEhr1qzR2LFjNXToUH300Ue31F66ywIAAABAMjZ69GibVZwyZYprXZEiRTyymOPHj9fAgQP1wAMP2HWff/65cuXKpdmzZ6tdu3batm2b5s+fr9WrV+uuu+6y+0ycOFFNmzbVm2++qbx582r69OmKiIjQp59+qjRp0qhcuXJav369xo0b5xGM/hcymQAAAADgxuFI/OVWzJkzxwaGDz30kHLmzKnKlSvr448/dm3fu3evjh49arvIxsicObPuvfderVixwt43P00X2ZgA0zD7BwQE2MxnzD61atWyAWYMkw3dsWOHzabeLIJMAAAAAEhi4eHhtnuq+2LWxWXPnj364IMPVKJECf3888/q3r27nnvuOX322Wd2uwkwDZO5dGfux2wzP02A6i516tTKmjWrxz5xHcP9MW4GQSYAAAAAJPGYzJEjR9pso/ti1sUlKipKd955p9544w2bxTRdV7t27WrHXyZHBJkAAAAAEDtKSuRlwIABOnfunMdi1sXFVIwtW7asx7oyZcrowIED9nbu3Lntz2PHjnnsY+7HbDM/jx8/7rH96tWrtuKs+z5xHcP9MXwqyDx79qytkPTUU0/Z5e2337YnGgAAAABSmqCgIDuViPti1sXFVJY14yLd7dy501aBjSkCZILAxYsXu7ab7rdmrGW1atXsffPTxFymamyMJUuW2CypGbsZs4+pOHvlyhXXPqYSbalSpTwq2fpEkGnmdylWrJgNLE0kbRZTwcisW7t2rbebBwAAAMCfmClGEnu5Bb1799bKlSttd9m///5bX3zxhZ1WpEePHna7w+HQCy+8oBEjRtgiQZs2bVKHDh1sxdhWrVq5Mp+NGze23WxXrVqlZcuWqWfPnrbyrNnPeOyxx2zRHzN/ppnqZMaMGZowYYL69Onje1OYmJPWsmVLWyHJDD6NSd2ajKY5WSaaBgAAAAB/dPfdd+u7776z3WmHDx9uM5dmyhIz72WMfv366dKlS3a8pslY1qhRw05ZEhwc7NrHTFFiAsv69evbqrJt27a1c2vGMONCFyxYYIPXKlWqKHv27Bo8ePAtTV9iOJxmUhUvS5s2rdatW6fSpUt7rDeTjJoSu5cvX761A16YlbANRPKWOp23W4AkcvH+5znXfiTD0i+83QQkoaip/TnffiKgTVdvNwFJKfMjPnm+Ix8sn+iPkWrmZqVUyaK7rOl/HDNo1d3BgweVMWNGr7QJAAAAAHDrkkV32UceecT2+33zzTdVvXp1u870EX7ppZf06KOPert5AAAAAPzJLY6ZRDIMMk1waQarmsGpZiymERgYaCcZHTVqlLebBwAAAADwpSDTVDAyVYvM5KO7d++260xl2XTpGGsHAAAAwB8HFfquZHH6nnzySV24cMEGlRUqVLCLuW2qI5ltAAAAAADfkCyCzM8++0yhoaHXrTfrPv/8c6+0CQAAAICfSmbzZPoar3aXPX/+vMwMKmYxmUz3OVwiIyM1b9485cyZ05tNBAAAAAD4SpAZEhJiC/6YpWTJktdtN+uHDRvmlbYBAAAA8FMpPNOYooPMX375xWYx69Wrp1mzZilr1qwexYAKFSqkvHnzerOJAAAAAABfCTJr165tf+7du1cFCxa0mUsAAAAA8KpkUbnGd3ktyNy4caPKly+vgIAAnTt3Tps2bbrhvhUrVpQ/ioyM0sSPFmvOT+t18tQF5cyeSa1b3Klnu9R1BeRm/ZsTf9YfK3fpwoUw3XVnYQ16qYUKF8zuOs4TT3+sVWv3ehz7kTb3aPgrrZL8OeHGjh07q7ETftDSZdsUGnZFhQpk1xvDHlWFcgXt9gWLN+irb5Zry7aDOnvusmZ/9aLKlM5/3XHWbdirt9+dp42b9isglUNlSuXT5Pe7KTg4DaffixzZcylN9xeVumotKTitog7tV/gbAxS1Y7PdnubJXkpdv5kcOXNLV68ocscWRXw0TlFbN147RoHCCnq2n1JVqGImE1bU7h2K+Hi8Itf9abenbtJawa+OjvPxLzWvKufZ00n0bHErPvpojt4aN0MdOjTWq6884bHN9Pbp+vQYLV26Ue+921sNGtzFyU2mPl5xXuN+O6cn7sqgVxpk0dnQSL279LyW7QvTkfORypouQPVLpNVzNTMrY3D0p9czoZHqN+e0dpyI0NnQKGVLl0r1SqRV79qZlSHo2ifciKtOvb/snOZsuayTlyKVI30qPXtfJrW9I4MXn7H/Wb12nyZP+0Obtx/RiZMX9N6YR9WgThmP9+s7Hy3RN7PX6PzFMN1ZsaCGvmw+k2Vz7WP+/37tzXn65Y8dCnA41LBuWb3at4nSpwuy2/9cs1dTv1yhTVsO6eKlcBUqkE1dnrhPLRvf4ZXnDPhckFmpUiUdPXrUFvYxt03QZN6csZn1pgiQP/r4s9/15cw/NXrYgypeNJc2bz2kAcNnKWOGYHVoV92erx4vTlPq1Kn0/ltPKEP6IE2d/oc6P/up5n7zgtKlvRZUPNz6bj33TAPX/bTBgV56VojLufOX9WinCbr37hL6+N1nlCVrBu3ff0KZM12bK/ZyaITurFxETRpW0sDhM+I8jgkwn+rxoZ55soEGvdxGqVIHaPuOw/bLHHhRxkxK+8GXilz7p0Jf7GqDvYD8heS8cM61S9TBvQp/e7iiDh+UIyhIgQ93VtpxU3SpXQPp7Bm7T9oxHyrq4H6FPt9BzvAwBT7cScFjPtTlRxrIefqkri6ep0t/LvV46KBXR8mRJogAM5nauGm3vpqxRKVKRX+ZFNtnn82nl48P2HQkXDPWX1SpHNf+bz1+MdIu/eqGqFj2QB0+d1VDfz5j101oHf1FsAkyTFD5fK3MypIuQAfOXNVrC87oXFiU3mx5LTDpPfukTl6O0oimWVUoJLWOX4pUHB+ZkMguh0WoVIncatviTvV8+avrtn/8+R/634w/NWpIa+XPm0UTPlyiLs99rnkzeiooKPq18eLgmTpx8qKmTOygK1ej9Mpr32nwG3P01oiH7PZ1Gw+oVPFc6tqhhrJnzWCD0ZeHfquM6YNVt2YprnFSYkymbwaZpotsjhw5XLdxvXUb96t+7TKqU6O0vW/+YM39eaM2bjlk7+87cErrNx3UjzOeV4liuey6oQMe0H2NRmruzxv0UKu7XccKDg5UjuwZOc3J1MdTFit37iwaOfwx17oC+a59wDBaNY++nof+OXXD44x8c7aeeLSWnn7y2hcKRQtHvzbgPWnaPy3n8aMKHznAtS7ySPT7OMbVhT+6bpvPjuET31Bgi4eUqlhpRa5ZIWXOooACRRQ28lWbwTQiPnhTadq0V0DRkoo8fVKKCJfzdPi1g4ZkUao7qyp81KtJ8TRxiy5dCtNLL76vEa89pQ8+mH3d9m3b9unTKXM1a+YI1ajZg/ObTF2KiNJLc05reJOsmrTsvGt9yRxp9E6ba72KCmZJrRdqZ1a/H07papRTqQMcyhwcoEfvvJaNzJc5tb3/6Z8XXOuW7gnV6oPhWtAtj0LSporeL8Sro538Vu3qJe0SF/PF/+dfrVD3J2upQe3o7OaYoW1UvfEYLfptu5o1rKDde09o6Yq/NXPqM6pQNp/dZ+CLzfT0C9PU7/lGypUjk7p1jh5KFqNju2pa9uffWvDrVoJM+BSvpTdMUR+Tpbxy5YqtIBsVFWXXxbX4q8oVC2nl6t3au/+kvb995xGt2bBPtf7/D1zElav2Z1DQtf9sTMYqTZrUWrN+v8exfvhpve6tP0LNHx6vt979WaFhEUn6XPDvlvy2WeXLFtBzL05RtboD1eqRsfp61opbOm2nTl/Qhk37lS1rBrXrMF7V6w3U410m6q91ezj9Xpb6vnqK3L5Jwa9NULofVijtp7OVusXD//ILgQp84BE5L5xX5N/bo9edO6Oo/XsU2LiV7W6rVKkU2OoRRZ0+qcj/73IbW2Dj1lJYmK7+Mj+RnhniY/jwqapdp5KqVy9/3bbQ0HD1ffE9DR7cSTlyhHCikzGTeaxdLFjVC1+bhu1GLoRHKUOaABtgxuX4hUgt3BGquwtGd500luwKVbncaTT5zwuq/e5hNf7wiMYsOauwK1EJ+jwQP4cOn9GJUxdV/Z5irnWm59kd5fJp3aaD9r75mSljsCvANKrfXVQBAQ5t3Oz5xaO7CxfDFZIpLZfIG1FSYi8pmNe/CgsMDLSVZQcNGuTtpiQ7T3eqpYuXwtTkwbeVKsChyCinej97v1o2qWS3Fy2cQ3lzh9igcfgrrZU2baCmTl+mo8fO2bECMZo3vkN584QoZ45M2rHrqN6cOF9795/Qu2Mf9+Kzg7uDh07py2+WqfPjddTtqfu1afMBjRjzrQIDU6l1y3tu+hjGu5Pmq1/vB1SmdD7N/mG1Oj39nn6c2V+FC0X3HEDSc+QtoMBWj+nKjCmK+HySAspUVNALA6UrV3R1/neu/VJVr6PgoW/bINJ56oRCe3e2wWWM0Bc6Knjk+0q/YJ0UFSXn2VMK69tFunAte+IusNmDurroB5vhRPIyd+4Kbd26VzNnvhbn9pEjp6ly5ZJqUJ8xmMnZ3K2XtfXYFX3T8b97jJy5HKkPlp3Xw5XSX7et7/enbDAZdtWpusWD9VqTa9X2D52N1NpD4QpK7dDENtl0JjRKw38+Y8d8vtHMs8cLvMcEmIb5oteduX/y/7eZOhpZs3hefzPkKXOmtK7fj23ews3atPUfDe/fMtHaDqTIINNo1aqVZs+erd69e9/y74aHh9vFXVDEFVffd1/208JN+mH+Br014mEVL5ZL23Yc0chxP9pgsXXzOxWYOpUmjm2vV1/7VvfUe02pUgWo2j3FbKbTGavIT4xSxXPbbrOduk/WgUOnVDA//0ElB84op81k9nmuub1ftnR+7dp9RF/NXHbTQWZUVPRVf6RtdbVtda/rOCtW7dSs71eq73MtEvEZ4F8FOBS1fbMt5GOv1a5tCihSQoGt2nkEmWbM5uXOD8gRkkWBLR5W8PDxCn36Idd4yqA+Q+Q8c1qhPR6TzJjMFg8pePSHCu3a1galHg9ZrpICihRX2IiXuDjJzJEjp/T6G5/r008HKCjo+oJci5es0co/t+i7b9/wSvtwc46cv6qRi85ocrscNgD8NxfDo9Ttm5Mqnj1QPWpkvm57//oh6lEjk/advqpxv53VqMVnNKRRdKAZ5XTanl9jW2RzFQx6ub5TL3x3SoMbZlFwYApPh/ixlX/tsWM2R7zygEoUy+nt5vgfxmT6fpBZokQJDR8+XMuWLVOVKlWUPr3ntzzPPffcDX935MiRtrutuyH9H9LQVx6Rrxvzznw93bGWmjW6wxUgHj5yRh9O+dUGmUb5Mvn0/Re9dOFimK5cuaqsWTLooY7vq7xbV4zY7ihfwP7cf5AgM7nIkSOTihXL7bGuaJFc+nnRxls6hhH7OMWK5NLhI2cTqKW4HSYAjNq322Nd1P7dSl2nkeeOYaFy/nPALuFbNijdlwuUuvlDujLtQ6WqUk2pqtfVpSZ3SZcv2d3D3xqmdHfdZ6vKXpn2kcehTAAauXOronZs4aIlM1u27NWpU+fVps2rHtXEV/+1XdOnL9Cj7RrowIHjuvuerh6/1+u58bqrSmn9738DvdBqxLblaIROXY5S2ynHXOsindJfB8P1xZqL2vBSftsL6VJ4lLp+fULp0phMZHYFpro+IM2RIZVdimYLtOM0H59+XN3vy6yc/78+V4ZUrgDTKJYt0H6ZfPRCpApnJchMDnJki85gnjp9UTndamCY+6VL5rG3s2fLqNNnov9+x7h6NVLnzoe6fj+GmRWge98vNKB3E7VqFt2DDfAlySLInDx5skJCQrRmzRq7uDPf3v1bkDlgwAD16dPHY11QxDylBGFhEXLE+hbFZCvjqsJr+v0b+w6c1OZt/+j57vff8LgmI2pQCCj5uPOOItq777jHun37Tyhfniw3fYz8ebMqZ47McR6n1n3XSqwj6UVuWquAgkU81gUUKCzn0X/+/RcDAuRI8/+ZruD/H+8V+/3vjJIcsT5kpk2n1PWaKGJSdOYUyUvVquX0w5xRHusGvPKRihbNo65PtVCWLBn1yCP1PLa3aNlfA/o/rrr1or9ghPdVKxSs77t4dpN9de5pFckWqKeqZrQBpslgPjXjhNKkcuj9B7P/Z8YzJnNpXLka/fPO/EH6eXuoLTCUPk30e91kPM3Hg9wZowsBwftMcUYTKK5YvUdl/j+ovHgxTBu2/KNH20b3SKpcoYDOXwjT5m2HVb5MXrtu5V97bU+kiuWvTUlmpjHp1me6Xux5vx5pTZd5r/nvtyuSc5BpAqZff/3VTmWSNu2tD2oOCgqyi4cLvt9V1qhbs4wmffqrHXdppjDZtuOwpkz/Q21bXvuD89OiTcoakt7us+Pvo3rjrR/VoHZZ1ahawm43XWJNl9va95VSSOZ0dkzmyHFzdfedhVW6RPQfQXhfx8fr6NFO4zXpk4V2ipKNmw/Ywj/DB10rDnP23CUdOXJGx09Ej7/buz86mMyePZNyZM9kv5Dp0rGuJk6ar9Il89r5Mb/7YbX27Duud97s7LXnBunKjKlKO+krBT7RTVeXzFOqshUV2PIRhY/5/7HowWmVpkN3XV22WM6TJ6K7y7Zpb+fWvPrLT3aXyM3r7dhLMw9mxNT3oqcwafGwHHnyK3LFrx6nOXW9plKq1Lqy4HtOfzKUIUNalSwZ3aMkRrq0QQoJyehaH1exn7x5s6tAfrrMJRfpgwJsBVl3aQMDFJI2er0JMLvMOKGwK06NaZFNF8OduhgePSWbmTPTBKG/7Q7VqUuRKp8njdIHBmjXySt685ezujN/GlcF2WZl09mxnCaA7Vkzsx3bOfaXs2pTMT1dZZPYpcvhOnDotEexn207j9gxleZzWId21fTBp7/ZuS3tFCaTFtusZoPa0bMEFCuSQzWrFdegN77XsP4tdOVqpF4bO1fN7i9vK8vGdJE1AWaHdlXtHJoxNTZMjQbzOQ7wFckiyDTdZbds2WJ/4pqBL7XQhEkLNWzUHJ06Y7pfZLLjK3t0vfYNt/njM+rteTp16qLNTD7QrLKefaqua7sZt7li1d/6/Mtluhx6RXlyZVbDeuX0bJdr+8D7KpYvqHfHddG4d37Uex/9rPz5suqVl1qrZbNrXygs+XWzBgz50nW/98uf2589n2mkXt2b2NudHq+jiIirdiqTc+cu22Dz00ndVbDAtTL6SHpR2zcp7JUeSvNMX6Xp1EPOI4cU/s4burrwh//fIVIBhYoquElrOTJnkfP8GUVt22THXkbt/Tt6n3NnFNq3i9I83VtpJ3xmK9BG7d2lsAHPKiqmAu3/C2z+oK7+tkC6eK0AGICktfVohDYejq7k3ujD6B5EMRZ1y2ODyODUDn2z4ZJGLT6riMjozOT9pdKqa9XogMMw2Usz7nPEwjN6aOoxG8Q2Lh09tyaSlslAdug+xXV/5Pjoyt2tm1XSqCFt7NyWpnq/mffy/MUwVbmjoD6Z8IRHnZA3hz9oA8uOPabaeVIb1iurgX2burbPnrteoWFX9OHUpXaJcc+dhfW/SU8m2XMFYzLjy+GMq+9lEitXrpztMlu1atWEOeCFWQlzHPiG1Hyz5y8u3v+8t5uAJJRh6Recbz8SNbW/t5uAJBLQxnO8MVK4zL5ZJyXymcQfnpDqw7VKqZLFaPFRo0bppZde0ubNcc/1BgAAAABJxgx8TuwlBfN6d1mjQ4cOunz5su644w6lSZPmurGZp09f6/8OAAAAAEi+kkWQOX78eG83AQAAAACSUX9P35UsgsyOHTt6uwkAAAAAgJQSZB44cOBftxcsWDDJ2gIAAADAz6XwMZN+EWQWLlzYzvF3I5GR0fNKAQAAAACSt2QRZK5bt87j/pUrV+y6cePG6fXXX/dauwAAAAD4HwdjMpM+yNyzZ4+KFi2qhGKqysZ21113KW/evBo7dqzatGmTYI8FAAAAAEg8txWjFy9eXHXr1tW0adMUFhamxFKqVCmtXr060Y4PAAAAANdhnsykDzLXrl2rihUrqk+fPsqdO7eeeeYZrVq16rYbcf78eY/l3Llz2r59uwYOHKgSJUrc9nEBAAAAAD4QZFaqVEkTJkzQ4cOH9emnn+rIkSOqUaOGypcvb8dRnjhx4paOFxISoixZsriWrFmzqmzZslqxYoU++OCD22kiAAAAANx+lJTYSwoWr8I/qVOntuMlmzVrpvfff18DBgzQiy++qFdeeUUPP/ywRo8erTx58vzncZYsWeJRXTYgIEA5cuSw3XLNYwAAAAAAfEO8Yui//vpLzz77rA0kTQbTBJi7d+/WwoULbZbzgQceuKnjVKhQQbVr17aLKShkfn/y5Mk2kwkAAAAASYoxmfFyW2lCE1BOmTJFO3bsUNOmTfX555/bnyYDaRQpUkRTp06181/+m02bNqlFixY6ePCgHXv51VdfqXHjxrp06ZI91ttvv62ZM2eqVatWt/fsAAAAAADJP5Npxkk+9thj2r9/v2bPnq3mzZu7AswYOXPmtNnIf9OvXz+bxfz9999Vp04dexzT9dYU/jlz5owtKDRq1KjbaSIAAAAA3B4ymUmbybx69arat2+vJ5544l/HW6ZJk0YdO3b812OZ6UnMeExTqdbMlfnRRx/Z7rcxAWuvXr1UtWrVW20iAAAAAMBXMpmmEM9bb71lg834On36tJ0CxciQIYPSp09vq8vGMLcvXLgQ78cBAAAAgJtGddmk7y5br149/fbbb0oI7lVl47oPAAAAAEjhhX+aNGmi/v3728I9VapUsRlIdy1btrzpY3Xq1ElBQUH2dlhYmLp16+Y6Xnh4+O00DwAAAADiNyYTSRtkmnGTMVVmYzOZyMjIyJs6Tuwxm48//vh1+3To0OF2mggAAAAA8JUgMyoqKkEe3EyDAgAAAAC+P6gQMTh9AAAAAADvB5mm8E+LFi1UvHhxu5hxmEuXLk24lgEAAACANzBPZtIHmdOmTVODBg2ULl06Pffcc3ZJmzat6tevry+++CJ+LQIAAAAA+NeYzNdff11jxoxR7969XetMoGkKAb322mt67LHHErKNAAAAAJB0GFSY9Kdvz549tqtsbKbL7N69e+PXIgAAAACAfwWZBQoU0OLFi69bv2jRIrsNAAAAAHwWYzKTvrts3759bffY9evXq3r16nbdsmXLNHXqVE2YMCF+LQIAAAAA+FeQ2b17d+XOnVtvvfWWvv76a7uuTJkymjFjhh544IGEbiMAAAAAJB3GZCZ9kGm0bt3aLgAAAAAAxDvIBAAAAIAUOyYTSRtkZsmSRQ7H9SferAsODlbx4sXVqVMnde7c+fZbBgAAAADwjyBz8ODBdq7MJk2a6J577rHrVq1apfnz56tHjx52GhMzbvPq1avq2rVrQrcZAAAAABIPmcykDzL/+OMPjRgxQt26dfNY/+GHH2rBggWaNWuWKlasqHfeeYcgEwAAAAD8yG3VTfr555/VoEGD69bXr1/fbjOaNm2qPXv2xL+FAAAAAJDUUVJiLynYbT29rFmz6ocffrhuvVlnthmXLl1SxowZ499CAAAAAEDK7i47aNAgO+byl19+cY3JXL16tebNm6dJkybZ+wsXLlTt2rUTtrUAAAAAkNgYk5n0QaYp5lO2bFm9++67+vbbb+26UqVK6bffflP16tXt/b59+8avZQAAAAAAn+NwOp1OpThrvN0AAEA8Rb3Wi3PoRwIGTfR2EwAkiio+eV6j3q6Z6I8R0HupUqrbHnK6e/duDRw4UI899piOHz9u1/3000/asmVLQrYPAAAAAJKWw5H4Swp2W0Gm6RZboUIF/fnnn3a6kosXL9r1GzZs0JAhQxK6jQAAAACAlBxk9u/f386TaYr7pEmTxrW+Xr16WrlyZUK2DwAAAACSliMJlhTstoLMTZs2qXXr1tetz5kzp06ePJkQ7QIAAAAA+EuQGRISoiNHjly3ft26dcqXL19CtAsAAAAAvIMxmUkfZLZr104vv/yyjh49KofDoaioKC1btkwvvviiOnToEL8WAQAAAAD8K8h84403VLp0aRUoUMAW/TFzZtaqVcvOkWkqzgIAAACAz2JMZrykvp1fMsV+Pv74Yw0ePNiOzzSBZuXKlVWiRIn4tQYAAAAA4H+ZzOHDh+vy5cs2k9m0aVM9/PDDNsAMDQ212wAAAADAZzEmM+mDzGHDhrnmxnRnAk+zDQAAAADgn26ru6zT6bQFf2LbsGGDsmbNmhDtAgAAAAAfSsXhtoLMLFmy2ODSLCVLlvQINCMjI212s1u3brdySAAAAACAvwaZ48ePt1nMJ5980naLzZw5s0cxoMKFC6tatWqJ0U4AAAAASBpx9NpEIgWZHTt2tD+LFClipysJDAy8lV8HAAAAAKRwtzUms3bt2q7bYWFhioiI8NieKVOm+LcMAAAAALyBRGbSD2k1VWR79uypnDlzKn369HaspvsCAAAAAPBPtxVkvvTSS1qyZIk++OADBQUF6ZNPPrFjNPPmzavPP/884VsJAAAAAEmFeTKTvrvsDz/8YIPJOnXqqHPnzqpZs6aKFy+uQoUKafr06Wrfvn38WgUAAAAA8J9M5unTp1W0aFHX+Etz36hRo4Z+//33hG0hAAAAACT1mMzEXlKw2woyTYC5d+9ee7t06dL6+uuvXRnOkJCQhG0hAAAAACBlB5mmi+yGDRvs7f79++u9995TcHCwXnjhBTteEwAAAAB8FmMyk35MZu/evV23GzRooO3bt2vNmjUqUaKEKlSoEL8WAQAAAAD8I5NpKsqWLVtW58+f91hvCv7Ur19f7dq109KlSxO6jQAAAACQtFFSYi8p2C09vfHjx6tr16622E9smTNn1jPPPKNx48YlZPsAAAAAACk1yDTjMBs3bnzD7Q0bNrTdZm/F2rVrtWnTJtf977//Xq1atdIrr7yiiIiIWzoWAAAAAMQbYzKTLsg8duyYAgMDb7g9derUOnHixC01wGQ/d+7caW/v2bPHdrlNly6dvvnmG/Xr1++WjgUAAAAA8KEgM1++fNq8efMNt2/cuFF58uS5pQaYALNSpUr2tgksa9WqpS+++EJTp07VrFmzbulYAAAAABBvzJOZdEFm06ZNNWjQIIWFhV23LTQ0VEOGDFHz5s1vqQFOp1NRUVH29qJFi+xjGAUKFNDJkydv6VgAAAAAAB+awmTgwIH69ttvVbJkSfXs2VOlSpWy680UJmauzMjISL366qu31IC77rpLI0aMsFOh/Pbbb/rggw/s+r179ypXrly3dCwAAAAASJAxmUiaINMEfcuXL1f37t01YMAAm4U0HA6HGjVqZAPNWw0MTcXa9u3ba/bs2TZALV68uF0/c+ZMVa9e/ZaOBQAAAADwoSAzZk7MefPm6cyZM/r7779toFmiRAllyZLlthpQsWJFj+qyMcaOHatUqVLd1jEBAAAA4HaRyEziIDOGCSrvvvtuJZS//vpL27Zts7fLlClju9ECAAAAAPwkyEwohw4d0qOPPqply5YpJCTErjt79qztKvvVV18pf/783m4iAAAAAH9CKjPpqssmhqeeekpXrlyxWczTp0/bxdw2FWfNNgAAAACA7/B6JtNUlDXFhGIq1Rrm9sSJE1WzZk2vtg0AAACAH6K4rG9nMs18mCaTGZuZDiVv3rxeaZMvmj59gerVe04VKnTUQw8N0saNf3u7SUgkXGv/wvX2cdWfUMCg5XI0fP7auiz55HhopBx95srRb6EcbV+T0scqnpe1gBwPj5aj77zofTp+IBW68/rjV2wqx9OfyzHgl+jjNe6b+M8JCYL3tv/gWiMxjBo1ys7w8cILL7jWhYWFqUePHsqWLZsyZMigtm3b6tixYx6/d+DAATVr1kzp0qVTzpw59dJLL+nq1ase+/z666+68847FRQUZGf+mDp1qu8FmaaKbK9evWzhnxjm9vPPP68333zTq23zFfPmrdDIkdPUo0cbfffd6ypduqC6dBmlU6fOebtpSGBca//C9fZxecrIcecDch7bdW1dYLAcj42X5JRzWi85pz4jpQqU45GxHl+bO9qNlQJSyfm/XnJ+0lk69nf0uvRZrx3r3nZy1H1GzuX/k3PS43JOe07OPX8m8ZPE7eC97T+41j4swJH4y21avXq1PvzwQztLh7vevXvrhx9+0DfffGN7ix4+fFht2rTxSOKZADMiIsL2JP3ss89sADl48GDXPnv37rX71K1bV+vXr7dBrBnC+PPPP/tWkNmpUyf7BO69914bLZvF3F67dq2efPJJZc2a1bUgblOmzNPDD9dV27Z1VLx4fg0b1kXBwUGaNes3TlkKw7X2L1xvHxaYVo7WQ+ScO0oKvXBtfYGKUkhuOb8fIR3fYxfn969JeUtLRapE75M2sxzZCsq57H/S8d3S6UNyLvlAjjRppZxFo/cJzihH3afl/H64tHmhdOaf6H13/uGd54tbwnvbf3CtkdAuXryo9u3b6+OPP/aYQvLcuXOaPHmyxo0bp3r16qlKlSqaMmWKDSZXrlxp91mwYIG2bt2qadOmqVKlSmrSpIlee+01vffeezbwNCZNmqQiRYrorbfesjN+9OzZUw8++KDefvtt3xqTOX68+UYXtysi4qq2bNmrZ55p6VoXEBCg6tXLa906t2/P4fO41v6F6+3bHE36SruWS3v/kmp0urYhVaDNYirSbZjI1QjJGSVHgTvkNPuHnpPz5H45KjaR8+gO6eoVyWREL56WjuyI/p2id0dXPsyUQ47uX0hp0kmHNsm5cKJ0/njSP2HcNN7b/oNr7eOSYExmeHi4XdzFJN1uxHSHNZnGBg0aaMSIEa71a9assUMQzfoYpUuXVsGCBbVixQpVrVrV/qxQoYJy5crl2qdRo0bq3r27tmzZosqVK9t93I8Rs497t1yfCDI7duzo7Sb4tDNnLigyMkrZsmX2WG/u79lz2GvtQsLjWvsXrrcPK9dAylNKzk+6XL/tny1SRJgc9Z+Vc8kkGyg66nWXIyC1nBmyuXYzXV8dD4+S4+VFNgDVpTNyftlHCvv/rGhIPskRIMd9HeX8ebwUflGOOk/L0X6CnB8+IUV5jq9B8sF7239wrfFfRo4cqWHDhnmsGzJkiIYOHRrn/mZ6R9Pb03SXje3o0aNKkyaNa0rIGCagNNti9nEPMGO2x2z7t33Onz+v0NBQpU2bVsk2yDSNzJQpk+v2v4nZ79a+AYhQUFCaBGgpAAC3IFNOORq+IOf056XI6K5HHi6flXPWQDmavCTHPQ9FB5CbF8l5ZHv07f/naPJidGA5tbt0NVyOyi3leGSMnJO7SBdPRQenqQIV9fPb0p5V9nec3w2Ro/cPUuEqEmMzASDZz5M5YMAA9enTx2PdjbKYBw8etDVrFi5cqODgYCV3XgkyTf/hI0eO2IpGJto2lZFiczqddr0ZoHrr3wB01dChz8gfZMmSUalSBVxX5Mfcz57d85sM+DautX/hevuoPKXlyJBV6jrFtcpmKQtVkuPutnK+UccGhc73HrJjLxUVGZ2F7P2DnGf/v/eJCRJLVJdzbCMp4rJd5fzpTTmK3G2ryWr5/6IDTePE3muPffmsdPmclNnzG2gkL7y3/QfXGv/lv7rGujPdYY8fP26rvsYwcdLvv/+ud9991xbmMeMqz54965HNNNVlc+fObW+bn6tWRX8x6b49ZlvMz9gVac19k/i72Sym14LMJUuWuAr5/PLLL4nwDcAW+Ys0aVKrXLkiWrFiixo0uNuui4qKsvcff7yht5uHBMS19i9cbx+19y9FTXrcc13LV6WT++VcPs0jW2nGXrqCSjOFSUzRnsD//4ba6Yx18Khr36wf3Bj9M1tB6cKJ6NvBGaV0maVz0V2ekDzx3vYfXGsfl8zmyaxfv742bdrksa5z58523OXLL79sp4UMDAzU4sWL7dQlxo4dO+yUJdWqVbP3zc/XX3/dBqsm2WeYzKgJIMuWLevaZ968eR6PY/aJOUayDjJr164d5+2E+wbAv7rKdu7cVC+/PEnlyxdVxYrF9NlnPyk0NExt2sTv3CL54Vr7F663DzKZxxN7Yq0LjQ4oY9bf0Uw6uS8685i/vO1eq5UzpFMHorcf2mzHXjoeGCjn0inSlejusgrJK/29PHqf0wfl3PG7HI16R1ewDb8sR71u0qn90r41Sfykcat4b/sPrjUSSsaMGVW+fHmPdenTp7dzYsas79Kli02+mWSeCRzNNJEmODRFf4yGDRvaYPKJJ57QmDFj7PjLgQMH2mJCMfFUt27dbGa0X79+dqYPkxz8+uuvNXfuXN8q/GOYtK5J3Zqo2mTh3HXo0MFr7fIVTZtW0+nT5/XOOzN14sRZlSlTSJ980l/Zs3sWA4Lv41r7F653ymSmJ5EJCNNmks4ekfOPz6Q/v7q2g6ku+0UfOwem4/GJUqrUtlusc8bLdr7MGM7Zw+Vo+Lwc7d6MznoeWGd/z3bBRbLGe9t/cK19WBKMyUxoZpoRM8uEyWSamjWmKuz777/v2p4qVSr9+OOPtpqsCT5NkGqKsA4fPty1j5m+xASUZs7NCRMmKH/+/Prkk0/ssW6Fw2kGP3qRmTDUzPVi5nwxEbf7+Exz+/Tp07dxVL7FBQBfF/VaL283AUkoYNBEzjeQIv3/HMA+xjmjSaI/huORn5RSBXi7AX379rWpWBNkmozmmTNnXMvtBZgAAAAAEA+OJFhSMK8Hmf/884+ee+45pUuXzttNAQAAAAD4epBp+vf+9ddf3m4GAAAAAEQLcCT+koJ5pfDPnDlzXLebNWuml156SVu3blWFChVs6V13LVu29EILAQAAAAA+E2S2atXqunXuVY3cC/+YSUYBAAAAIMmk7ERjygwyY09TAgAAAABIGbw2JtNM7GkmAz1//vx1286dO6dy5cpp6dKlXmkbAAAAAD9mplVM7CUF81qQOX78eHXt2tXOjRlb5syZ9cwzz2jcuHFeaRsAAAAAwMeCzA0bNqhx48Y33N6wYUOtWbMmSdsEAAAAAMyT6aNB5rFjx66rJOsuderUOnHiRJK2CQAAAADgo0Fmvnz5tHnz5htu37hxo/LkyZOkbQIAAAAAxmT6aJDZtGlTDRo0SGFhYddtCw0N1ZAhQ9S8eXOvtA0AAAAA4ENTmBgDBw7Ut99+q5IlS6pnz54qVaqUXb99+3a99957dn7MV1991VvNAwAAAOCvUnbx15QbZObKlUvLly9X9+7dNWDAADmdTrve4XCoUaNGNtA0+wAAAAAAfIfXgkyjUKFCmjdvns6cOaO///7bBpolSpRQlixZvNksAAAAAP4sgFSmzwaZMUxQeffdd3u7GQAAAACAlBBkAgAAAECy4SCT6ZPVZQEAAAAAKQ+ZTAAAAABwRyYzXshkAgAAAAASDJlMAAAAAHBHJjNeCDIBAAAAwJ2DDp/xwdkDAAAAACQYMpkAAAAA4C6AKUzig0wmAAAAACDBkMkEAAAAAHcU/okXMpkAAAAAgARDJhMAAAAA3FFdNl7IZAIAAAAAEgyZTAAAAABwx5jMeCGTCQAAAABIMGQyAQAAAMAd82TGC5lMAAAAAECCIZMJAAAAAO6oLhsvZDIBAAAAAAmGTCYAAAAAuKO6bLyQyQQAAAAAJBgymQB8hzPS2y1AEgoYNJHzDaRETqe3W4Ck5PDR000mM17IZAIAAAAAEgyZTAAAAABwR3XZeCGTCQAAAABIMGQyAQAAAMBdgK8OJk0eyGQCAAAAABIMmUwAAAAAcEd12XghkwkAAAAASDBkMgEAAADAHdVl44VMJgAAAAAgwZDJBAAAAAB3jMmMFzKZAAAAAIAEQyYTAAAAANwxT2a8kMkEAAAAACQYMpkAAAAA4I7qsvFCJhMAAAAAkGDIZAIAAACAO6rLxguZTAAAAABAgiGTCQAAAADuyGTGC5lMAAAAAECCIZMJAAAAAO7IZMYLmUwAAAAAQIIhkwkAAAAA7gLIxcUHZw8AAAAAkGDIZAIAAACAO8Zk+l6Q+c4779z0vs8991yitgUAAAAAkHAcTqfTqSRWpEgRj/snTpzQ5cuXFRISYu+fPXtW6dKlU86cObVnz57beIQ1CdRSAMmKM9LbLUBScqTifAMpUdJ/9IQ3Oe7yyfPv3NAr0R/DccdEpVReGZO5d+9e1/L666+rUqVK2rZtm06fPm0Xc/vOO+/Ua6+95o3mAQAAAAB8KZPprlixYpo5c6YqV67ssX7NmjV68MEHbSB668hkAikSmUz/QiYTSJnIZPoXX81kbnw+0R/DUXGCUiqvV5c9cuSIrl69et36yMhIHTt2zCttAgAAAAD4aJBZv359PfPMM1q7dq1HFrN79+5q0KCBV9sGAAAAwA8FOBJ/ScG8HmR++umnyp07t+666y4FBQXZ5Z577lGuXLn0ySefeLt5AAAAAABfmiczR44cmjdvnnbu3Knt27fbdaVLl1bJkiW93TQAAAAA/oh5Mn07yIxhgkoCSwAAAADwbckiyDx06JDmzJmjAwcOKCIiwmPbuHHjvNYuAAAAAH7I4fVRhT7N60Hm4sWL1bJlSxUtWtR2ly1fvrz27dsnM7OKmSsTAAAAAOA7vB5kDhgwQC+++KKGDRumjBkzatasWcqZM6fat2+vxo0be7t5PmP69AWaPPlHnThxTqVLF9SgQR1VsWJxbzcLCezixVBNmPCNFi36S6dOnVPZsoX1yisdVLFiMc61D4uMjNLEd7/VnDnLdPLkOeXMmUWtW9fUs90fkOP/x4QsWLBaX321RFu27NPZcxc1+7sRKlOmkLebjtuwevU2+/d68+a9OnHirN57r7caNLjbtb1//0n67rvfPX6nRo2Kmjy5P+fbB33xxUJ9+eUi/fPPSXu/RIl8evbZNqpdu5Jrn3Xrdurtt7/Wxo27FRAQYN/b5noHB6fxYstxqyZOnKV33/vWY12RInk0/6c37e0ZM5boxx+Xa8vWvbp0KUyrV32kTJnSc6KTK8Zk+naQuW3bNn355ZfRjUmdWqGhocqQIYOGDx+uBx54wE5lgn83b94KjRw5TcOGPak77iiuzz77SV26jNL8+W8pW7bMnL4UZODAj7Vr10GNGdPdBiJz5vyhzp3f0Lx5Y5UrV1ZvNw+36eOPf9SXXy7W6FHPqHjxfDb4GPDKx8qYIa06dGhk97kcGq47q5RUkyb3auCgyZxrH3b5crhKlSqktm3rqGfPt+Pcp2bNOzRy5DOu+2nSeP2/a9ym3Lmz6sUX26lQodxyOqXZs39Xjx5v6bvvRqpEifw2wHzqqdF65pkHNGhQJ6VKFaDt2w8oIIVPb5BSmWs65dMBrvupUqdy3Q4NC1fNmhXt8ta4GV5qIZA0vP6/Vvr06V3jMPPkyaPdu3erXLly9v7Jk9Hf+uHfTZkyTw8/XNd+YDGGDeuiX39dr1mzftPTT7fk9KUQYWERWrBgld5/v6/uvruMXder14P65Ze1+uKLRerd+2FvNxG3ad26Xapf/07VqROd2cifP4fmzl2hjZv2uPZp9UAN+/PQoROcZx9nMljuWay4mKAyR46QJGsTEk+9elU87vfu/YjNbK5fv8sGJOZL4ieeaOTx/3XRonm5JD7KfElwo/dup45N7M8//9yaxK3CbSGTGS9eH9FatWpV/fHHH/Z206ZN1bdvX73++ut68skn7Tb8u4iIq9qyZa+qVy/vWme62pj75oMrUo6rVyNtt8qgoECP9UFBabR27Q6vtQvxV7lyCa1csVV79x6x97dv3681a3eqVq2KnF4/tWrVNlWr1k2NGvXVkCGTdebMBW83CQnA/A2fO3e5zWab970Z9rBhw9+211G7dkNUvXo3Pf74cP31V/SUbvA9+/cfU42aPVS/wQvq++J7OnyYhAn8k9czmaZ67MWLF+1tMy7T3J4xY4ZKlChBZdmbYD54mP+0YneLNff37DmcOBcNXpEhQ1r7oeT9979T0aL5lD17Zju2w3wbXrBgbq6KD3v66ea6eClUTZq+bL8FN+/p3i88qJYt7vN20+AFpivd/fffbTPaBw8e07hxX6tr19GaMWO4fX3A9+zYccAGkeHhV5QuXbAdh1u8eH7799t4991Z6tfvMZUpU1izZy9Vp05v6McfR6tw4TzebjpuQcU7itlu7mYc5onjZrz1t2r/+HD9MGe0/T8cPiaAv7c+HWSaqrLuXWcnTZp0S78fHh5uF3dBQRE2uwOkNGPGPKtXXvlQtWr1sB82TeGfZs2q22w2fNdPP/2pH35Yrrfe7G4/eG7bvl8j35juKgAE/2Le0zFKlSpolwYNemvVqq2qVu1arxX4jiJF8mr27JG6cOGyfv55lV5+eZKmTRukqCin3f7II/VcQ17M3/UVKzbbIS99+7bzcstxK2rXutYNvnSpgrrjjmKqW+95/TT/Tz30YPT1BfyFz4foI0eOVObMmT2WkSOnyF9kyZLRBhumy407cz97dsbzpDQFC+bStGmDtW7dp/r114maOXOE7UZboEBObzcN8TBm7Fd6umtzNWtWTaVKFbDjLzt2aqQPP/qB8woVKJDL/q033fDgm8wYW1P4p3z5ojZwNFXgP/98vmvsXrFi+T32L1YsH90sUwBTOdZkow/sP+rtpuC2OJJgSbm8EmRmyZJFWbNmvanlZqZAOXfunMcyYEBn+dN/XOXKFdGKFVtc66Kioux907USKZPpbmWyXOfOXdQff2xU/fqehSXgW8JCI+SIVUkyVUCAnP+f5YB/O3r0lM6evUghoBTEZDBNTQXTJdr8Ld+713N4y759R5QvX3avtQ8Jw0xTYrq8U8QL/sgr3WXHjx+fYMcKCgqyiyf/6irbuXNT2/XGfENq5ks0U5iEhoapTZva3m4aEtjSpRtsCXwz3uPAgWMaM+YLW4WQa+3b6tatpEmT5ihvnux2CpNt2/ZrytT5atu2lmsfE2QcOXJKx4+fsfdjigSZsbl8gPG9D54HDlzLbJiKwdu27VPmzBnsYsbnNWp0j+2NYj6gjh37hQoVymXHasL3vPXWV6pV6w7lyZNdly6F2rH0prCTmQfTzIPbpUtzTZw4U6VLF7LzY5o5Uk1NhXfeecHbTcctGj16uurWvVN582a3f6snvjvLFmNs3jy6C7yZF/fkybP2/29j586DSp8+2L42QkIycL6TG6rLxovD6TQfWVOaNfI306b9rMmT59o/YOY/qYEDO9o5M5GyzJu3UuPGfaWjR0/b/5AaNrzblsPPmDGd/IIzUinRxYuhmvDOLC1a9JdOnTpvMxvNmlVVj2dbu+ZH/Pbb3+3cmbH17NFavXq1UYrkuDa/XEpipi/o0GHEdetbt66loUOftHMobt26XxcuXLKvhfvuq6Dnn3/YfqEA3/PKKx9p5crNOn78rP1bbbrEd+3a0l7XGB99NEfTpy/QuXOXbFfaF198VHfdVVopVkr86Gmmp+kzUatXb7dfCmbNmlFVqpRS7xcetkNdjIkTZ+nd97697vdGvvF0yv6y2HGXfJFz17X5ThOLo8RIpVTJKsgMCwtzzZkZI1OmTLdxJP8LMgG/kEKDTPhXkAn4veTz0RNJwVeDzL9fTfTHcBR/XSmV1wv/XLp0ST179lTOnDltdVkzXtN9AQAAAAD4Dq8Hmf369dOSJUv0wQcf2LGVn3zyiZ0vM2/evPr888+93TwAAAAAfofqsj49T+YPP/xgg8k6deqoc+fOqlmzpooXL65ChQpp+vTpat++vbebCAAAAADwlUzm6dOnVbRoUdf4S3PfqFGjhn7//Xcvtw4AAACAX1aXTewlBfN6kGkCzL1799rbpUuX1tdff+3KcIaERE9SDAAAAABJxhGQ+EsK5vVnZ7rIbtiwwd7u37+/3nvvPQUHB6t379566aWXvN08AAAAAICvTmFi7N+/X2vWrLHjMitWvN2Jp5nCBEiRmMLEvzCFCZAyJa+PnkhsvjqFyZ6hif4YjqKJ/xh+l8lcsWKFfvzxR491MQWAunXrpnfffVfh4eHeah4AAAAAwJeCzOHDh2vLli2u+5s2bVKXLl3UoEEDDRgwwI7JHDlypLeaBwAAAMBfUfjHN4PM9evXq379+q77X331le699159/PHHdjzmO++84yoCBAAAAADwDV6bJ/PMmTPKlSuX6/5vv/2mJk2auO7ffffdOnjwoJdaBwAAAMB/eb0+qk/z2tkzAWbM1CURERFau3atqlat6tp+4cIFBQYGeqt5AAAAAJAsjBw50ibhMmbMqJw5c6pVq1basWOHxz5hYWHq0aOHsmXLpgwZMqht27Y6duyYxz4HDhxQs2bNlC5dOnscM5vH1atXPfb59ddfdeeddyooKMgWY506darvBJlNmza1U5YsXbrUjsE0T7RmzZqu7Rs3blSxYsW81TwAAAAA/iqZjcn87bffbAC5cuVKLVy4UFeuXFHDhg116dIl1z5myKGpa/PNN9/Y/Q8fPqw2bdq4tkdGRtoA0yT4li9frs8++8wGkIMHD3btY5KAZp+6deva4Y0vvPCCnnrqKf3888++MYXJyZMn7ZP+448/bKRtnmTr1q1d2814TZPZfP3112/j6ExhAqRITGHiX5jCBEiZmMLEv/jqFCb7RiT6YzgKD7zt3z1x4oTNRJpgslatWjp37pxy5MihL774Qg8++KDdZ/v27SpTpoyd1cPEVT/99JOaN29ug8+YYYuTJk3Syy+/bI+XJk0ae3vu3LnavHmz67HatWuns2fPav78+cl/TGb27Nn1+++/2xNigsxUqVJ5bDcRuFkPAAAAAEnqFjONtyM8PPy6KRtNF1Wz/BcTQxlZs2a1P9esWWOzm2amjhilS5dWwYIFXUGm+VmhQgWPujiNGjVS9+7d7awflStXtvu4HyNmH5PR9KkRrZkzZ74uwIw5YSaaBgAAAICUOM4yc+bMHsvNTOEYFRVlg7777rtP5cuXt+uOHj1qY6eQkBCPfU1AabbF7OMeYMZsj9n2b/ucP39eoaGhyT+TCQAAAADJU+JnMgcMGKA+ffp4rLuZLKYZm2m6s5phh8kVQSYAAAAAJLGgm+wa665nz5768ccf7bDD/Pnzu9bnzp3bFvQxYyfds5mmuqzZFrPPqlWrPI4XU33WfZ/YFWnN/UyZMilt2rS+010WAAAAAJIVR0DiL7fA1Go1AeZ3332nJUuWqEiRIh7bq1SpYqd/XLx4sWudmeLETFlSrVo1e9/83LRpk44fP+7ax1SqNQFk2bJlXfu4HyNmn5hj3CwymQAAAACQjPXo0cNWjv3+++/tXJkxYyjNOE6TYTQ/u3TpYrvfmto2JnDs1auXDQ5N0R/DTHligsknnnhCY8aMsccYOHCgPXZMRrVbt25699131a9fPz355JM2oP36669txVmfmMIkcTGFCZAiMYWJf2EKEyBlSokfPZHypjA5ODrRH8NR4OWb3/cG1W6nTJmiTp062dthYWHq27evvvzyS1u11lSFff/9911dYY39+/fbarK//vqr0qdPr44dO2rUqFFKnfpa7tFsM3Nubt261XbJHTRokOsxbrq9BJkAfAZBpn8hyARSJoJM/0KQmSBBpq+huywAAAAAJHF12ZSMwj8AAAAAgARDJhMAAAAA3N1i9Vd44uwBAAAAABIMmUwAAAAAuIlqrrg5ZDIBAAAAAAmGTCYAAAAAeCCTGR9kMgEAAAAACYZMJgAAAAC4o7psvJDJBAAAAAAkGDKZAAAAAOCBMZnxQSYTAAAAAJBgyGQCAAAAgDvmyYwXMpkAAAAAgARDJhMAAAAA3FFdNl5SZpAZcd7bLUBSckZyvv3FyY3ebgGSUr7anG9/cn6vt1uApJKxIOcaSOFSZpAJAAAAALeN6rLxwZhMAAAAAECCIZMJAAAAAO6oLhsvZDIBAAAAAAmGTCYAAAAAuKO6bLyQyQQAAAAAJBgymQAAAADggeqy8UEmEwAAAACQYMhkAgAAAIA7qsvGC5lMAAAAAECCIZMJAAAAAB7IxcUHZw8AAAAAkGDIZAIAAACAO8ZkxguZTAAAAABAgiGTCQAAAADuyGTGC5lMAAAAAECCIZMJAAAAAB7IxcUHZw8AAAAAkGDIZAIAAACAO8ZkxguZTAAAAABAgiGTCQAAAAAeHJyPeCCTCQAAAABIMGQyAQAAAMCdg1xcfHj97IWGhury5cuu+/v379f48eO1YMECr7YLAAAAAOCDQeYDDzygzz//3N4+e/as7r33Xr311lt2/QcffODt5gEAAADwx+qyib2kYF4PMteuXauaNWva2zNnzlSuXLlsNtMEnu+88463mwcAAAAA8KUxmaarbMaMGe1t00W2TZs2CggIUNWqVW2wCQAAAABJK2VnGlN8JrN48eKaPXu2Dh48qJ9//lkNGza0648fP65MmTJ5u3kAAAAAAF8KMgcPHqwXX3xRhQsX1j333KNq1aq5spqVK1f2dvMAAAAA+GN12cReUjCvd5d98MEHVaNGDR05ckR33HGHa339+vXVunVrr7YNAAAAAOBjQaaRO3duuxw6dMjez58/v81qAgAAAEDSY0xmfHg9TxsVFaXhw4crc+bMKlSokF1CQkL02muv2W0AAAAAkKSYwsS3M5mvvvqqJk+erFGjRum+++6z6/744w8NHTpUYWFhev31173dRAAAAACArwSZn332mT755BO1bNnSta5ixYrKly+fnn32WYJMAAAAAP7W4dOnef3snT59WqVLl75uvVlntgEAAAAAfIfXM5mmouy7776rd955x2O9WedebdYffTHjN30543f9c/iUvV+iWB49262Zatcsb++fOHlOY976VstXbNOly2EqUjiXunVtokb33+k6xpatB/Tm299q05b9ShUQoIYNKqt/vweVPl2w154X/ttHkxforQnfq0P7unr15Qd16J9Tqt9kcJz7jn+zi5o0vFPbdxyyv7dm3W6dOXtJ+fJmVbuHaqrj43U55V62esNhTZ6xQZt3ndSJU5f13vCGalCjiGt7qXofxvl7Lz19r55qV0mHjl7Q+/9bo5XrDuvk6cvKmS29Wt5fXN3a36k0gansvnsOnNWQ8Uu1e/8ZXbgYoZzZ06l5veLq2bGKAlNH74PkYfXqbZo8+Udt3rxXJ06c1Xvv9VaDBne7tvfvP0nfffe7x+/UqFFRkyf390JrcasuXgrXhEmLtOjXrTp15qLKlsyrV/o2U8Vy+e32iR8t1twFG3X02DkFBqZSudL51PvZ+3VH+QKuY5w9d1mvjf1Rv/yxXQEOhxrWK6dX+zZT+nRBXJBk7uLFUE14Z5YWLfpLp06dV9kyhfTKq0+oYoWidvvJk+f05ptf6Y9lm3XhwmXddVcpDRrYQYUL5/Z20xHXmEz4bpA5ZswYNWvWTIsWLXLNkblixQodPHhQ8+bNkz/LnSuLXnyhlQoVyimnU5o9Z4V6PPeBvvvmVZUonlcvvzJV5y9c1gcTuytLSAb9MG+1XnjxY836aoDKlimoY8fPqnPX8WrS+C4NeqWdLl4K0xujv9aAgZ/pnXHPePvp4QY2bt6vr775Q6VK5nOty5M7i/5Y8obHfjNmLtPkqYtUq0ZZe3/z1gPKmjWjxo7sZPdfu36PBg//QqlSOfT4o3U43150OeyqShXLprZNSqvnkAXXbf9j5hMe93//84BeffM3NaoV/aFkz4EzckY5Nbx3TRXKl1k7957WoHG/KzT0ql7uHv13MzB1gFrdX0LlSuZQxvRptH33KbuP0+lUn6fuTaJniptx+XK4SpUqpLZt66hnz7fj3KdmzTs0cuS1v9Np0nj9v2vcpIEjvtOu3cc0ZtiDypkjk+b8tF6de3yqeV8/r1w5M6twwewa/FILFciXVWHhVzT1y2V6sucULfyur7JmSW+P8eKgr3Xi5AVNebezrlyN1CvDv9XgN2brrRGPcB2SuYGDJmvXrkMaM7qbcubMojlzlqlz51GaN3eUvd+jx3ilDkyl99/vrQzp02rq1J/U+clRmvvjKKUjAYAUxOv/a9WuXVs7duzQ+++/r+3bt9t1bdq0seMx8+bNK39Wr05Fj/u9n2tlM5vrN+61Qea69Xs0ZNCjqlghOiPy7DNN9dn/FtvspQkyf/1tk1KnTqUhr7ZTQEB0z+hhg9qrZdvXtP/AcRUqmNMrzws3ZjLSLw2YqhFDH9MHH813rU+VKkA5smf22HfRkg1q0uhOV1b6wdbVPbYXyJ9d6zfs1YJFGwgyvaz2vQXtciM5sqbzuL94+X7dWymvCuTNZO/XuqegXWKY9XsPntWXP2x1BZlmXcz+Rr7cGbVqw2H9teloIjwjxEft2pXs8m9MUJkjRwgn2seEhV3Rgl+26P032+vuO6P/b+71dH39snS7vpi1Sr27368WjT17aQ14oalmfr9GO3YdVbV7imn33uNaumKXZn7WXRXKRmc/B77YXE+/8Ln6Pd9EuXJce58jeQkLi9CCBav1/nu9dffd0UPBevVqo19+WacvvlysVg/U0PoNf+vHH0aqRInoazt0aCfdV6On5s5dqYce4gvhZMXh9VGFPi1ZnD1T5MdUkZ01a5ZdRowY4fcBZmyRkVGa+9NqXQ6NUOU7ov/jqlypqH6av0Znz12y072Y7eERV3TP3SXt9oiIqwoMTO0KMI3g4ED7c83av5Ps+uLmDX/9a9WuWU7Vq14/TtmdyVpu237ousAytgsXQxWS2TOAQfJmusP+tvKAHmz676+BC5cilDnjjbvO7f/nnJauPqi7K+ZJhFYisa1atU3VqnVTo0Z9NWTIZJ05c4GT7gOuRkbZ/6+D0kT/XxsjKChQa9fvv27/iCtXNeO71cqYIVilSkZ3l1y36YAyZQx2BZhG9XuKKSDAoY2bDybBs8Dtuno1Mvr6B8W6/sFptHbNTvu5zN53224+o6VJE6g1a3Zw4pGieD2TOWXKFGXIkEEPPfSQx/pvvvlGly9fVseOHeXPduz8R+0eH2ODx3TpgvTe+GdUvFh0hnf8m13V+6VPdG+NvkqdOkDBwWn07vhurgxl1XtLadSb3+iTKQvU4fF6Cr0crrfGf2e3nTh53qvPC9eb+9Nf2rrtoGZ+2e8/T8/Mb5erWNHcurNSdHfKuJjusj/9vEYfvvssp9uHfLdgp9KnC1TDmtfGbMYVQE6bvUUvP1P1um3tes7Wll0nFXElUo80L6PnO18b6wffULNmRd1//93Knz+HDh48pnHjvlbXrqM1Y8Zw26sByVeG9EGqXKGg3p/8i4oWyaHsWTPox583av2mAyqYP5trP5PZ7PPqDIWGXVGO7Bn06budlTUkuqvsyVMXlTVLBo/jml5JmTOl1YlTF5P8OeHmZciQVpUrFdf7789W0aJ5lT17Zv04d4XWr9+lggVzqWjRPMqbN5veGve1hg97UmnTBmnqZ/N19OhpnThxjlOd7DAmMz68/r/VyJEjlT179uvW58yZU2+84TkGLS7h4eE6f/68xxIeHqGUokiRXJo981V9Pf1lPfpwLb088DP9vfuw3Tbh3Tl2TObUj1/QrK9eUecODeyYTBOYGqZL7agRnTTls0WqdPdzuq/uy8qXL7uyZ8skB4OZk5UjR8/o9dEzNXZUp+u+AY2rO86PP/2lB1tHd5OMy85dh/Xs8x+qR7emqlG9TCK0GIll1k871KJ+cQXdYAzesROX9NTL89S4dlE93Pz6a/v24Ab67sO2euvV+vp15QFN/noDF8vHNGtWXfXrV1GpUgVtQaAPP3xRmzbt0apVW73dNNyEMcMftGOhazUdrQr3DdH/ZixXs4YVbSYyxr13FdXs6T311eSnVbNaSb3wylc6dZoAMiUYM6Zb9PWv/ZwqVOys//1vgZo1q2YzlqZ32cR3nte+fUd1z73dVKlyF/3551bVqlVRDrfXB5ASeD2TeeDAARUpcv039oUKFbLbbiZIHTZsmMe6IQM7aOigTkoJ0gSmdmUmy5crpE2b9+vzab/oqScbatqXv+rH7wbbYNIoXSq//lrzt6Z/9auGD25v17Vodo9dTp48r7Tp0sghh6Z+vsiO10PyYcbRnjp9QW0eGeVaZ7rcrLbX8zdt+muCK4Mxf+E6hYVGqFWLuIu5/L37iDp1fUePtL1Pzz7dJMmeA+Lvr41H7FjL8YMbxLn92MlL6tD3B1Uul0uv9akV5z55ckZnQIoXzqLIqCgNHrdUTz5UkQyYDytQIJeyZMmo/fuPqVq16OriSL5MxnLaR13t8BZTcC9n9kx6YcBXKpAvi2ufdGnTqFCBbHapVKGgGrYZZ8dlPtO5trJny6DTZy5e1w3z3PlQ5cjmmeFE8mMyltOmDdTly2G6eDFMOXOG6IXe76pAgRx2e/nyRfT97NdtZdkrV64qa9ZMeujhIXY9khkSMr4dZJqM5caNG1W4cGGP9Rs2bFC2bNe6ltzIgAED1KdPH491QY4VSqminE5FRFxRaGh0ttb9m1HDBCKmCmVs2bNHFwqY+d0ymym7rxrZreTEdG3+YdarHusGDP6fihbJpa6dG3oECLO+W6F6dSrYSrKx7fr7sDo+9Y5atbxXvZ9rmSRtR8KZ+dN2lSuZXaWLZYszg2kCzHIlsmtkvzrXvffjYqpSX70aZf9uMImJ7zp69JTOnr1IISAfYwJJs5jg8I+Vu/RSr0Y33DcqymnHZxqmu+35C2HavO0flS8TXWV85V977D4V3aY5QfJmKsWa5dy5S/rjj0166UXPysAZM0bXSzBZTTOd0fPPPeillgIpNMh89NFH9dxzzyljxoyqVSv6m/nffvtNzz//vNq1a/efvx8UFGQXDxFplBKY8ZO1apRXnjxZdOlSuH6ct0qrVu/U5Em9VLRIbhUqmEODh03Xyy+2VUhIBi1asl7LVmzzGIM37YtfVLlSMTue08ynOWbcLPV9obUyZaIYTHKSIX2wSpbwrKacLm2QQjJn8FhvqgKb7OZH73WPs4tsx6cmqMZ9ZdS5Qz07j6ph5keNKyBF0rkUekUH/rk23ubQkQva9vdJW7gnb67oa3PxUoTm/7ZHL3erFmeA+USfOXZfs/30ubDrKtPOWbTLjs0uVSSrnTtz084TeuvjP9WkblHmyUxmLl0K04ED16r+Hjp0Qtu27VPmzBns8u67s9So0T3Knj3EjskcO/YLFSqUy47VRPJnKsOa7pJFCmXXgUOnNWbCTypaOIfatKxis5uTPv1V9WqVVo7sGXXm7GVN/2aljp04r8b1o7PUxYrkVM1qJTTo9e80bMADunI1Sq+N/UHNGlagsqwPWLp0o8xX/UWK5NaB/cc0ZuxXdixmmzbRn3F/mv+nsmbJZMdm7th5UG+8Pk0N6ldRjRoVvN10JL9RhT7N60Hma6+9pn379ql+/fpKnTq6OaZSaocOHW5qTGZKZrpPvvzqFB0/cV4ZM6ZVqRL5bIB5X/XoeRE/er+n3ho/W916vq/LoeEqWCCHRr3eUbVrXftDtXHzPk18/0dduhxus2LDBrdXqxbXFwuBbzBZzNy5QuIcZ/nzwnW2i9WcH1fbJUa+vFm1ZP5rSdxSuNu844Q69PnBdX/kB9G9LVo3KqlRL9e1t+f+8rfNPDavV+y6k7dszSHt/+e8XWo9Ms1j244l0XMppk7l0CdfrtfeQ+dsCtMEpI+3Lq9OD/LBJbnZvHmPOnQY4bo/cmT0NW3dupaGDn1SO3ce0OzZS3XhwiU7r95991XQ888/bCtQIvm7cDFM495boKPHzykkU1o1rFdOvZ9taL/siYqM0p59J/Td3LU2wDTVvyuUzafpH3VViWK5XMd487WHbWDZ8dlPFeBw2GOYaUyQ/Jmq7qZYlynmExKSXg3vv1u9ez9kx2MaJ46f1ahRX+jUqXO2d8IDD9TQs91bebvZQIJzOM3XbcnAzp07bRfZtGnTqkKFCnZM5m2L+CUhm4bkzhnp7RYgqZzcyLn2J/lqe7sFSErn93K+/UXGG88bjBTIcY980uUfE/8x0qXcL4+8nsmMUbJkSbsAAAAAAHxXsggyDx06pDlz5thqshERntOPjBs3zmvtAgAAAOCPGJPp00Hm4sWL1bJlSxUtWlTbt29X+fLl7RhN04v3zjvv9HbzAAAAAAC+FKKbKUhefNFMNL1JwcHBmjVrlg4ePKjatWvroYce8nbzAAAAAPjjPJmJvaRgXg8yt23bZivJGqa6bGhoqDJkyKDhw4dr9OjR3m4eAAAAAMCXgsz06dO7xmHmyZNHu3fvdm07efKkF1sGAAAAwC+RyfTNINNkKi9duqSqVavqjz/+sOuaNm2qvn376vXXX9eTTz5ptwEAAAAAfIfX5slMlSqVjhw5oosXL9qlYsWKNug0Qeby5ctVokQJW1n2tubLZJ5M/8I8mf6DeTL9C/Nk+hfmyfQfzJPpX3x1nsywBYn/GMENlVJ5rbpsTGxrqsq6d52dNGmSt5oEAAAAAPDlKUwcKbyqEgAAAAAfRJziu0FmyZIl/zPQPH36dJK1BwAAAADgw0HmsGHDlDlzZm82AQAAAABiocelzwaZ7dq1U86cOb3ZBAAAAABASggyGY8JAAAAIFlyeG2mxxTBa2fPSzOnAAAAAABSYiYzKirKWw8NAAAAAP+CMZnxQR4YAAAAAJAyCv8AAAAAQLLDmMx4IZMJAAAAAEgwZDIBAAAAwANjMuODTCYAAAAAIMGQyQQAAAAAdw4ymfFBJhMAAAAAkGDIZAIAAACAO6rLxguZTAAAAABAgiGTCQAAAAAeGJMZH2QyAQAAAAAJhkwmAAAAALijumy8kMkEAAAAACQYMpkAAAAA4IFcXHxw9gAAAAAACYZMJgAAAAC4Y0xmvJDJBAAAAAAf8N5776lw4cIKDg7Wvffeq1WrVik5IsgEAAAAgOvCpMRebs2MGTPUp08fDRkyRGvXrtUdd9yhRo0a6fjx40puCDIBAAAAIJkbN26cunbtqs6dO6ts2bKaNGmS0qVLp08//VTJDUEmAAAAAMQek5nYyy2IiIjQmjVr1KBBA9e6gIAAe3/FihVKbij8AwAAAABJLDw83C7ugoKC7BLbyZMnFRkZqVy5cnmsN/e3b9+u5CZlBplp6srfmBfoyJEjNWDAgDhfmEhZ/PZ657v27Z2/8Ntr7af8+npnqiJ/4tfX2g9xvX1R4v9NGjlyqIYNG+axzoy3HDp0qHydw+l0Or3dCMTf+fPnlTlzZp07d06ZMmXilKZwXG//wbX2L1xv/8G19i9cb8Q3k2m6y5rxlzNnzlSrVq1c6zt27KizZ8/q+++/V3LCmEwAAAAASGJBQUE2OeS+3KhnQ5o0aVSlShUtXrzYtS4qKsrer1atmpKblNldFgAAAABSkD59+tjM5V133aV77rlH48eP16VLl2y12eSGIBMAAAAAkrlHHnlEJ06c0ODBg3X06FFVqlRJ8+fPv64YUHJAkJlCmNS6GShM8QD/wPX2H1xr/8L19h9ca//C9UZC6dmzp12SOwr/AAAAAAASDIV/AAAAAAAJhiATAAAAAJBgCDJ9RJ06dfTCCy94uxlIpvbt2yeHw6H169d7uymIxVyX2bNn3/C8/Prrr3YfM8fVzeBvAeC/pk6dqpCQEG83A4mM/9OREhBkJqBOnTrZD4sxS7Zs2dS4cWNt3LgxIR8GyZip9NWrVy8VLVrUDvIvUKCAWrRo4TGnUWIwj3PkyBH9X3tnAqxT/f/xr33flyxly76GGFvKUkJjK9lCiEKWyh4/21BRSTSEIWSLmFARxSSytJDKUlKIsbRS9s5/Xp+Z8/yf53qu+9zruZZ736+Zwz37ec7nu32+n+WUL18+QYqLuHEyr1WrlskuW7ZsEkMS6gdefPHFkO1MNLBdJA3I7tizZ09XqFAhq/f58uVzjRo1cps3b45ockncnNB2M24Lx6ZNm0yu12NMF7NPF+JWREpmlKFxomFgYZCZOnVq99BDDyX4ehcuXIjq84nEnXnkI7mffPKJmzhxotu9e7ella5Xr57r3bt3gq55+fJl+9BuXKRKlcoGOZQ3cWvJnI8rIzspIEmH9OnTu5deesn98ccfN/pRRCLx8MMPu6+//trNnTvX7d+/361cudK8DH777Te981uYbt26uXXr1rkjR45csW/OnDn2bcKKFSsm+nOoTxdJASmZUcaf0WTh2zVDhgxxhw8ftllPGDx4sCtZsqTLmDGjWT5GjBjhLl68GDh/1KhRdt6sWbNc0aJFbbDic+nSJUtZjMUjd+7cdq7neYH98+fPtwYwS5Ysdv/27du7EydOhDzfd999Z0pv1qxZ7bh77rnHHThwwPahzIwZM8bdfvvt9jv8b+8EE9fzJ2d69eplisL27dttAMJ7KleunH04d+vWrXbMq6++6ipUqOAyZcpkM5Wcc+bMmStcoRiwlC1b1uRw6NAhV6RIETd+/HjXtWtXkxuz5zNmzAjrWsPfKDmQI0cO2451BZBnnTp17B5Y2ikLvvz9SQ3KWP78+a3sFS5c2L3wwgvX8S0mPZnDqVOnXMuWLa3elChRwuTrE87qjDWEASvHI0MsJLEpLO+//761CQsWLLB12ptHH33UZJwzZ07XvHlzKxM+lIUWLVq4l19+2eRMOUAhVj2OHg0bNrQ2+Gp1591337WyQh2nfr/yyish++Oq85HIWiQO1FWsWkwk0NbSTvJR9KFDh7pmzZqZ7IA6T9321+G9995zVapUsfaVPnT06NHWt/vE1UfEhLEF/T73On/+vER+jdAn5smTx/riYJDB0qVLTQn97LPPbOyUIUMGk1Hfvn3dP//8EzgWI0PTpk1tP+O4hQsXWhl47bXXAsfs3bvX+mLKAX39+vXrQ6zf4dxlv/32W9e4cWOXOXNm+yZix44drW8R4mZFSmYiQqP09ttvu+LFi9tADhgs0Hh9//33bvLkyW7mzJlu0qRJIef9+OOPNgBZvnx5SAPDjCmWKga0nEtnhDLqwyBx7NixbteuXdZQ0Uj5ygX8+uuvrm7dujaowfLy5Zdf2gDG7+C4JgMdBp+4gzCwpcP84YcfAteI5PmTI7///rspcAzWGRzExI+hSZkypXv99ddN2UeeyGHQoEEhx/777782eEG2HJc3b17bjmwYTDB7zsADV619+/ZdcS86PcoPsJ8OD1kBHSEK0BdffGGWdp6HwYlvLeXZUIDeeecdOxfFJXiAJOIvc2AgiTJAvWrSpInr0KGDnR8O6nyDBg1s4PH555/bgAYXLqzaMWHw0q5dO5MT16QNoN5STxkEo6wyIMHDItgrYsOGDTa5wP+UQ+p0zEGVuDYrBArilClTwlpEaHspD23btjXrN5OLTNjFlMHV6nykshbRh/fMQj8bTrHbsWNHwPJF++uvI6dOnTq5fv36WR/65ptvmszHjRsXODeSPiJ4kgFlB5fKZcuW6TvZUYAxFjJCLsGT+CiYtME1a9a0OsakIu35kiVLrI0O/mYh5x89etQmEOmLmRwKnvDnOkz0MYm4bds22//888/HObFRv359V7lyZeu/6XuOHz9u7YgQNy2eiBqdO3f2UqVK5WXKlMkWXm/+/Pm9L7/8MtZzJk6c6FWtWjWwPnLkSC9NmjTeiRMnQo679957vTJlynj//fdfYNvgwYNtW2zs2LHDnuH06dO2PnToUK9o0aLehQsXwh5foEABb9y4cSHbqlWr5vXq1Svi50+ubNu2zd718uXL43Xe0qVLvVy5cgXW58yZY9fZuXNnyHGFCxf2HnvsscA65SBv3rzetGnTbP3gwYN23tdff23rGzZssPU//vjjqvc/efKkHbd7925b79Onj1e/fv2QciauTeYcM3z48MD6mTNnbNuHH34YVlbt2rXzateuHev1aAv69evnTZ061cuWLZu3cePGwL758+d7pUqVCpHf+fPnvQwZMnhr164NtFOUp0uXLgWOad26tdemTRuJOgrwfps3b25/16hRw+vatav9vWLFCpMztG/f3rv//vtDzhs4cKBXtmzZiOt8JLIWiceyZcu8HDlyeOnTp/dq1apl/euuXbsC+5E1Mg+mQYMG3vjx40O2IUfGCfHpI6j3e/fu9e644w6vb9++aq+jzJ49e0x+tM0+99xzj9XHbt26eT169Ag5ftOmTV7KlCm9s2fPBs5l/OXzww8/2LZJkybZOm1/6tSpvWPHjgWOWbduXUiZidmnjx071nvggQdC7nv48GE7Zt++fdF+BUJEBVkyowyuM1giWLA4MtOMe8Mvv/xi+5n1ql27trlSMRM6fPhwc4cMBtcb3DViUqNGjZC4LWbUsDL6Fg5mx7F44FbF7Pa9995r2/3r80zMeqZJk+aKa//9998288azBcP6nj17AuuRPH9yJHjG82rgEoOVqmDBgiYj3F2I4cF6GRyjFy7mI3gb5QAZxHSHjgvKC5Yv3LRwmfatlL4MsXxTTkqVKmUuQB999FG8rp+ciFTmMWWH1ZN3H5vsfEvm1cBq8cwzz1jskF/PAS8GPCEoW761BTfKc+fOhbhF46aJtc0Ht9n4liURN3gkYI0KbkOB9XBtbXB7Hledj1TWInHAkkWfiecHli2sVrjBXs0jAJkRkuLLi6V79+5m7fT7gEj6iLNnz1pf3qpVK/NSUTx3dCldurQlZJs9e7atU8+wQuMqiwyRcbAMGefhDXTw4EHzNMAaSlnwwZuNsAcfjsHjiPrsg7v11eC+eJ4E35fnBNV3cbMiJTPKMICkQWGpVq2auTzioohbKa5vuLThLrd69WpzgcJFIqZrUzjXu7jgHjR0DF5xncM9Z8WKFbbPvz7xAddCpM+fHCHOjo6eOIvYwH2ZeA8GjrjQMCnwxhtv2L7gd4icwg0aYk4OcEwkSYGCYRICN03KI246LMH3p2Oko8TtmoEMrjiPPPJIvO6RXIhE5gmRXST1FJcpJqIYBAUru7jok4jIn+jyFxKTEKOdkOcRCYfwBNplYvUSwtXkFKmsReJBPN39999vrs5btmyxSbqRI0fGejwyw3U+WF64SzO5wLUi7SMIeSHul36YMBgRfVAokcHp06fN7fnOO++0CT1k+OSTT4bIEAUQGXJMYsF96b9j1nfuSzsjxM2IUlEmMgwKiLFgwE4nhJUy2Pfet3BGgq8Q+JBYhIEuFgkGusx2kjafGTLAbz8YOi5m1YnliTl4QTktUKCAxfUEW0ZY92fYrvX5kzJYEBhMMiDAAhhzooB4CgYMDBCJs6JMALGPiQHWUAi2ilA+mEFFwWQWHIgliQlloU2bNragYDJLj2LKbxTxk3lCvmdHPSVelsFobDCYoRyRHIj6P3Xq1MAkAd4GxPEiR3HjoU0miRreAT5lypQJfOrCh3USRwVbmK+GZH3zQRy1n7iFPjZmHDUyow1mEjockfYR7CPRH5MJeE9hRaX/FtGDCVZiZ4l7nzdvnsVDM55DhsTTxiZD6jl5LpiEZxLIt4QGJ27jGOJpiakkgQ/4cbuxwX1RevE+UhZ5casgS2aUIQkA381jwSWK7+f5M1AohLglLl682NwbCO73rY2RwLkkbaGTWrRokSWVoBEEXGRRLNj2008/mQsP1qhgCEzHLZZkEyigzIDRUfmJJAYOHGjuXQxS2UZmXGbK/Htc6/MndVA2GFSglNMZ8H4pA7wnXJvplFDwfRnx7qdPn54oz8JkAB0iM91kH6QM4q5DAiqSDNDpkVCC8hQMyaQoW0xaYBEh2QEuPfr4d8JknhCwejHgINELiSWQxbRp067IIohCgvsU9+3fv79tw9OAzNNkGcW9C6s0A1CU4HAJaETiQ6ZQ5EKZ8HnuuedsIoE2mnrG5B8TBQMGDIj4upL1jYMJO5KwkNiPOko9o62cMGGC1T1AGUDGjAV8BeN///ufKSxMIJHYh7aC/pSwE4hPH8FkBF5LlSpVsmfhPiJ64I7KRCvtMe7MfhJFMuwz4c54yrckkjHYT/yDCytW5h49eljIFMomfwd7KGH9ZqKwc+fOVn6YYPLLQGyuzySYY7KXcBf6B8Zga9eudV26dAmbFE6Im4LohHYKP+EDr9RfsmTJYolzSBAQnNyBIP7MmTNbog0CwQniD078U6lSpbDJPkjA89RTT3lZs2a1hAPDhg0LCfhfuHChV6RIES9dunRezZo1vZUrV4YEjgOJCQgez5gxoz0fwewHDhywfZcvX/ZGjRrlFSxY0JIP8Rx+cpJInz+5c/ToUa93796WtCNt2rT2Lps1axZIIPDqq69akgeSczRq1MibN29eSNIXP6lDTLienzTAB/lQXsIlCYAxY8Z4+fLl81KkSGFl008uQLIoykjFihUtaUxwsoEZM2Z4d911lyWuopyRqOKrr75KxDeW9GUeLgEIMkbWsSVpQi4kE0FO2bNnt7Li7/cT//h8//33lhDm2WeftXWSSXTq1MnLnTu3nV+sWDGve/fu3l9//XVFYhofrsd1xbUT7v1SPykbwV0u/QKJfmhrCxUqZEnU4lPnI5G1SBzOnTvnDRkyxKtSpYrVZfpTkjCR4Ovff/+1Y+h/ixcvbglekKXPmjVrrG7TB9DGVq9e3dpdn/j2ERcvXvRatWpl7frx48cl8iiyZcsWe/dNmjQJ2b59+3ZL3MU4iL6SvjQ4aSJ9QuPGja1OInvGZrTR06dPDxxDgiASvNEulC5d2lu1apXdi/IRW5++f/9+r2XLltYnUD44r3///kr8JG5aUvDPjVZ0hRBCCCGESGrgRUIYk5/UKRxYM/luJl5GiRnbKcT1REqmEEIIIYQQUYBQFEJUcJXH1ZbvnJKgCdd4Px8GoUa45BKGhGJJWBIhLeHyJAhxq6LEP0IIIYQQQkQB4mqHDRtmcbV8hobPoRA/G5xwkay1xHeS54I4euI4SfgkRFJClkwhhBBCCCGEEFFD2WWFEEIIIYQQQkQNKZlCCCGEEEIIIaKGlEwhhBBCCCGEEFFDSqYQQgghhBBCiKghJVMIIYQQQgghRNSQkimEEOKG8vjjj7sWLVpICkIIIUQSQUqmEEIkQ6UuRYoUtqRNm9YVL17cjRkzxl26dOmGKIuTJ092b731VoLvW6RIkcDvCbfwXEIIIYS4fqS+jvcSQghxk/Dggw+6OXPmuPPnz7sPPvjA9e7d2z4WPnTo0CuOvXDhgimjiUW2bNmu6fwdO3a4y5cv299btmxxDz/8sNu3b5/LmjWrbcuQIUNUnlMIIYQQkSFLphBCJEPSpUvn8uXL5woXLux69uzpGjZs6FauXBlikRw3bpwrUKCAK1WqlG3fvXu3q1+/viltuXLlcj169HBnzpyxfaNGjXJz58517733XsCCuHHjRtt3+PBh9+ijj7rs2bO7nDlzuubNm7uff/45Vgvofffd5/r27esGDRpkx/OcXD828uTJY8ewcDzkzZvX3Xbbba5OnTpu5syZIcfv3LnTnu/HH3+0df6eNm2aa9y4sf22YsWKuWXLloWcE9dvEEIIIcT/IyVTCCGEKVdYLH0+/vhjswauW7fOrV692v3zzz+uUaNGLkeOHGY5XLp0qVu/fr17+umn7fgBAwaYEoaF9NixY7bUqlXLXbx40c7LkiWL27Rpk9u8ebPLnDmzHRd8v5igsGbKlMlt27bNTZgwwdx5eZb4gPLYtWtXs9gGw3rdunXNTdhnxIgRZgHdtWuX69Chg2vbtq3bs2eP7UvobxBCCCGSK1IyhRAiGeN5nimLa9euNSulDwrerFmzXLly5WxZuHChO3funJs3b54rX768HTt16lQ3f/58d/z4cVO6UFR9CykLLrZLlixx//33n12rQoUKrkyZMqbkHTp0KGDpDEfFihXdyJEjXYkSJVynTp3c3XffbYpvfMFKirK8ffv2gMLIb0H5DKZ169buiSeecCVLlnRjx461+02ZMsX2JfQ3CCGEEMkVxWQKIUQyBOskiiFKFwpU+/btQ1xSUaaC4zCx6lWqVMmUT5/atWvbuShxuKaGA8sgbqlYAYNBYT1w4MBVlcxg8ufP706cOBHv34m7b9OmTd3s2bNd9erV3apVqywOFaUymJo1a16xjlvttfwGIYQQIrkiJVMIIZIh9erVszhEFEkUsdSpQ7uDYGXyWiBms2rVqm7BggVhYyljgyREMV1fUWgTAhbKjh07ukmTJpkFsk2bNi5jxoyJ/huEEEKI5IqUTCGESIagRAbHJMYFLqJ8ZoTYTF8BJTYxZcqUgcRAKKx+llefKlWqmLspiXj8bK/XmyZNmtgzo1SvWbPGffrpp1ccs3XrVnPLDV6vXLnyTfMbhBBCiFsJxWQKIYSIE5LhpE+f3nXu3Nl9++23bsOGDa5Pnz5mIfRdZfle5TfffGPus6dOnTJXXM7LnTu3ZWMlac7BgwctjpHssUeOHLkubz5VqlQWm8nnWYjxjOkaCyQywqV2//79FgtKDKef1Ohm+A1CCCHErYSUTCGEEHGCeynJgX7//XdXrVo198gjj7gGDRpY8h+f7t27m1WTpDm4kWLp5Dwsh4UKFXKtWrUyi2i3bt0snvF6WgW5J5lgu3TpEnb/6NGj3eLFiy0WlORGixYtcmXLlg389pvhNwghhBC3Cik8UgsKIYQQSRgskCjFfO8yZpIi4j1XrFgR8q1OIYQQQiQcxWQKIYRIspBJ9uTJk5Y5l4yysWXBFUIIIUT0kLusEEKIJAtur4ULF3Z//vmnmzBhwo1+HCGEECJZIHdZIYQQQgghhBBRQ5ZMIYQQQgghhBBRQ0qmEEIIIYQQQoioISVTCCGEEEIIIUTUkJIphBBCCCGEECJqSMkUQgghhBBCCBE1pGQKIYQQQgghhIgaUjKFEEIIIYQQQkQNKZlCCCGEEEIIIaKGlEwhhBBCCCGEEC5a/B+/vWl2r3zBHAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建交叉表分析\n",
    "cross_tab = pd.crosstab(chipo['category'], chipo['protein'], \n",
    "                        values=chipo['item_price_sum'], aggfunc='sum')\n",
    "\n",
    "print('\\n类别与蛋白质交叉分析（销售额）：')\n",
    "print(cross_tab.fillna(0).round(2))\n",
    "\n",
    "# 可视化热力图\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(cross_tab.fillna(0), annot=True, fmt='.0f', cmap='YlOrRd')\n",
    "plt.title('Category vs Protein Revenue Heatmap')\n",
    "plt.xlabel('Protein Type')\n",
    "plt.ylabel('Category')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. 数据洞察总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "CHIPOTLE 数据分析洞察报告\n",
      "================================================================================\n",
      "\n",
      "【核心指标】\n",
      "总订单数: 1,834\n",
      "总商品数: 4,972\n",
      "总营业额: $39,237.02\n",
      "平均订单价值: $21.39\n",
      "平均每单商品数: 2.52\n",
      "\n",
      "【最受欢迎商品】\n",
      "最畅销: Chicken Bowl\n",
      "\n",
      "【最高收入商品】\n",
      "收入最高: Chicken Bowl\n",
      "\n",
      "【类别分析】\n",
      "Bowl           : $15256.10 (38.88%)\n",
      "Burrito        : $13180.94 (33.59%)\n",
      "Chips          : $ 4985.86 (12.71%)\n",
      "Tacos          : $ 3628.27 ( 9.25%)\n",
      "Beverage       : $ 1994.37 ( 5.08%)\n",
      "Salad          : $  191.48 ( 0.49%)\n",
      "\n",
      "【蛋白质偏好】\n",
      "Chicken        : 1654 份\n",
      "Steak          :  734 份\n",
      "Veggie         :  217 份\n",
      "Barbacoa       :  204 份\n",
      "Carnitas       :  186 份\n",
      "\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "# 生成数据洞察报告\n",
    "print('='*80)\n",
    "print('CHIPOTLE 数据分析洞察报告')\n",
    "print('='*80)\n",
    "\n",
    "print('\\n【核心指标】')\n",
    "print(f'总订单数: {chipo[\"order_id\"].nunique():,}')\n",
    "print(f'总商品数: {chipo[\"quantity\"].sum():,}')\n",
    "print(f'总营业额: ${chipo[\"item_price_sum\"].sum():,.2f}')\n",
    "print(f'平均订单价值: ${chipo.groupby(\"order_id\")[\"item_price_sum\"].sum().mean():.2f}')\n",
    "print(f'平均每单商品数: {chipo.groupby(\"order_id\").size().mean():.2f}')\n",
    "\n",
    "print('\\n【最受欢迎商品】')\n",
    "top_item = chipo['item_name'].value_counts().index[0]\n",
    "print(f'最畅销: {top_item}')\n",
    "\n",
    "print('\\n【最高收入商品】')\n",
    "top_revenue_item = chipo.groupby('item_name')['item_price_sum'].sum().idxmax()\n",
    "print(f'收入最高: {top_revenue_item}')\n",
    "\n",
    "print('\\n【类别分析】')\n",
    "for cat, revenue in category_revenue.items():\n",
    "    pct = (revenue / category_revenue.sum()) * 100\n",
    "    print(f'{cat:15s}: ${revenue:8.2f} ({pct:5.2f}%)')\n",
    "\n",
    "print('\\n【蛋白质偏好】')\n",
    "protein_pref = chipo[chipo['protein'] != 'None'].groupby('protein')['quantity'].sum().sort_values(ascending=False)\n",
    "for protein, qty in protein_pref.items():\n",
    "    print(f'{protein:15s}: {qty:4d} 份')\n",
    "\n",
    "print('\\n' + '='*80)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
